In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import mpl_toolkits as mplot3d
import scipy as sp
pd.set_option("display.max_columns", None)
from tqdm import tqdm
tqdm.pandas()
In [2]:
def lighten_color(color, amount=0.5):
    """
    Lightens the given color by multiplying (1-luminosity) by the given amount.
    Input can be matplotlib color string, hex string, or RGB tuple.

    Examples:
    >> lighten_color('g', 0.3)
    >> lighten_color('#F034A3', 0.6)
    >> lighten_color((.3,.55,.1), 0.5)
    """
    import matplotlib.colors as mc
    import colorsys
    try:
        c = mc.cnames[color]
    except:
        c = color
    c = colorsys.rgb_to_hls(*mc.to_rgb(c))
    return colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])

DATA CLEANING AND WRANGLING¶

Typeform Data¶

In [3]:
d0 = pd.read_excel("risk literacy_985 responses.xlsx", sheet_name = "643 above median").rename({"#":"ResponseId"}, axis = 1)
d0 = d0.loc[ (d0["Please state your current occupation."] != "Investment Professional, i.e. stock broker/trader; financial planner/advisor; portfolio manager; investment banker; stock analyst; venture capital/private equity; insurance agent, etc.") ]
d0
Out[3]:
ResponseId Please indicate your Gender. Please mark your age (in years) What is currently your highest Education? Please state your current occupation. How do you describe your willingness to take financial risk in general? Given the number of years that you have held various investments and the amount of investing you might have done, what degree of investment experience in the stock market do you have? If an expert tries to worry or scare me, i.e. a financial advisor about my financial situation, I choose another expert. I only buy a financial product I understand. I trust doctors. When I want to buy a bigger item like a refrigerator or an expensive item of clothing, I wait a month to see whether I still want it and only buy it then. I always keep in mind that everything I do on the web could be used to my disadvantage. In my household, we/I spend: In my household, we/I _Distribution 1_\n\nHow risky do you perceive the investment to be? _Distribution 2_\n\nHow risky do you perceive the investment to be? _Distribution 3_\n\nHow risky do you perceive the investment to be? _Distribution 4_\n\nHow risky do you perceive the investment to be? _Distribution 5_\n\nHow risky do you perceive the investment to be? Distribution 6\n\nHow risky do you perceive the investment to be? Distribution 7\n\nHow risky do you perceive the investment to be? Distribution 8\n\nHow risky do you perceive the investment to be? *Mumbai * A = 9 out of 10000 *OR * B = 1 out of 1000 *Bengaluru * A = 0.7% *OR * B = 0.099% *Kolkata * A = 0.61% *OR * B = 6 out of 10000 HIV test Fingerprint DNA test Cancer screening test Professional horoscope A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%. Which of the following statements is true? There is an official prediction that the national stock market will grow 2% annually over the next 5 years. This means that… Imagine you are told that the price of the stock Soya Ruchi increases from INR 60 to INR 120 after the company merger. What does this mean? It is predicted that Indigo Bank has 30% chance of default next year. Which of the following alternatives is the most appropriate interpretation of the statement? The probability that the economy will go into a recession this year is 30%. If the economy goes into recession, the probability that the stock market will decrease is 80%. If the economy does not go into a recession, the probability that the stock market will decrease is 23%. What is the probability that the economy goes into recession given that the stock market decreased? A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from: Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000. This implies: Imagine that we flip a fair coin 1,000 times. What is your best guess about how many times the coin will come up heads in 1,000 flips? \n\n\_\_\_\_\_\_ times out of 1,000. In the Bingo Lottery, the chance of winning a $10 prize is 1%. What is your best guess about how many people will win a $10 prize if 1,000 people each buy a single ticket for Bingo Lottery?\n\n\_\_\_\_\_\_ person(s) out of 1,000. In a sweepstakes, the chance of winning a car is 1 in 1,000. What percentage of tickets for the sweepstakes wins a car?\n\n\_\_\_\_\_ % of tickets About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only] Approximately what percentage (%) of people who die from cancer die from colon cancer, breast cancer, and prostate cancer taken together? The following figure shows the number of men and women among a group of smartphone users. The total number of circles is 100. \n\nHow many more men than women are there among the 100 people using a smartphone? In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die? Please indicate your approximate annual personal income from all sources for last year Please provide a rough guess (in Indian Rupees) of the worth of your household's assets. Please do not forget to correct it for your debts, such as a mortgage or any loans you might have. uid Response Type Start Date (UTC) Stage Date (UTC) Submit Date (UTC) Network ID LTA
2 6458w9qj8sqw7ldtt56457hzeduqz2qb Female 36 - 45 Post Graduate Employee/Consultant in Other than the Finance ... 6 10 Completely Just about Completely Just about Completely all or more than the household income, even th... have an emergency fund, and spend as it feels ... 7 3 6 7 6 7 6 7 B A B Yes Yes Yes Yes Yes The lower the quality of the study, the more l... the growth rate over five years will be betwee... the stock price increased by 100% 30% of central bankers think that Indigo Bank ... 80% 100 in 10,000 people prior to the intervention... The medication increases recovery by 100% 10 100 10 59 out of 100 20 10 Crosicol 35,00,001 - 45,00,000 10 MXzTR1725956643Vmr completed 2024-09-10 08:25:58 NaN 2024-09-10 08:33:57 942e517f31 5.543981
3 9woo3kquplxhob9whhbfvhleezgy3m8l Male 25 - 35 Post Graduate Employee/Consultant in Other than the Finance ... 5 3 Somewhat Somewhat Somewhat Not at all Somewhat all or more than our household income, because... write down budget and spending, and have an em... 4 2 4 1 1 7 6 3 A B A Yes Yes Yes No No Irrespective of the quality of the study, futu... the growth rate over five years will be betwee... the stock price increased by 50% Banks similar to Indigo will default 30% of th... 30% 5 in 100 people prior to the intervention to 6... The medication increases recovery by 2% 500 10 10 59 out of 1000 50 10 Hertinol 5,00,000 - 15,00,000 50000 MXhzL1725956594UqF completed 2024-09-10 08:25:48 NaN 2024-09-10 08:35:16 dcc2ff7969 6.574074
4 bago2g5wtk2g5u2h0xbago2g00a6b3jx Female 18 - 25 Post Graduate Student 3 2 Somewhat Just about Completely Completely Just about less than half of the household income and sav... spend as it feel right, and do not have an eme... 3 4 6 3 1 7 7 5 B A B Yes Yes Yes No No The higher the quality of the study, the more ... the growth rate will be 0.4% on average each year the stock price increased by 50% The bank will default on 30% of repayments in ... 80% it is not possible to determine which of the a... The medication increases recovery by 100% 500 10 10 9 out of 59 25 60 Hertinol 5,00,000 - 15,00,000 4000000 MXE5b1725956481kmw completed 2024-09-10 08:25:12 NaN 2024-09-10 08:36:22 ed3799240f 7.754630
5 huy0uxnebqrbqdegrz2sxhofkhuy0ux0 Female 25 - 35 Under Graduate Home Maker or not employed 5 0 Somewhat Moderately Moderately Somewhat Not at all all or more than the household income, even th... have an emergency fund, and spend as it feels ... 5 4 3 6 1 2 7 3 A A B Yes Yes Yes Yes Yes The higher the quality of the study, the more ... the growth rate over five years will be exactl... the stock price increased by 100% 30% of the banks customers will default next year 30% 5 in 100 people prior to the intervention to 6... None of the above is implied. 20 1 1 59 out of 100 50 40 Can’t say < 5,00,000 500000 MXWUf1725956389QtP completed 2024-09-10 08:25:04 NaN 2024-09-10 08:34:00 b9b7c6efb9 6.203704
7 bm66xvxw1reh3fbm668aomqq4gckt8ao Male 25 - 35 Post Graduate Employee/Consultant in Other than the Finance ... 7 10 Completely Completely Completely Completely Completely all or more than the household income, even th... have an emergency fund, and spend as it feels ... 7 7 7 7 7 7 7 7 A A A No Yes Yes No Yes The higher the quality of the study, the more ... the growth rate over five years will be exactl... the stock price increased by 100% Banks similar to Indigo will default 30% of th... 50% 100 in 10,000 people prior to the intervention... The medication increases recovery by 50% 800 500 60 59 out of 1000 30 50 Crosicol > 45,00,000 400000 MXnNH1725956452gcb completed 2024-09-10 08:24:01 NaN 2024-09-10 08:37:41 097676ebd8 9.490741
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
635 7my3wgtsbfwf8wr4f90f7my3wy5i8juf Male 56 - 65 Under Graduate Employee/Consultant in Other than the Finance ... 6 7 Just about Just about Just about Just about Just about all or more than our household income, because... write down budget and spending, but have no em... 5 5 5 5 5 5 6 6 B B A No Yes No No Yes Irrespective of the quality of the study, futu... the growth rate over five years will be betwee... the stock price increased by 50% 30% of the banks customers will default next year 50% 5 in 100 people prior to the intervention to 6... The medication increases recovery by 100% 1000 1000 100 9 out of 10 50 50 Crosicol 5,00,000 - 15,00,000 999900 MXX1J1724688179GOm completed 2024-08-26 16:04:51 NaN 2024-08-26 16:14:59 4ae93bad32 7.037037
637 ukj87cqk7sxt1a977ukj7dlqa19b34pg Female 25 - 35 Post Graduate Employee/Consultant in Other than the Finance ... 4 5 Somewhat Moderately Moderately Not at all Somewhat more than half of the household income and sav... write down budget and spending, and have an em... 6 6 6 2 3 1 3 5 A A B Yes Yes Yes Yes Yes Irrespective of the quality of the study, futu... the growth rate over five years will be betwee... the stock price increased by 50% 30% of the banks customers will default next year 30% it is not possible to determine which of the a... The medication increases recovery by 2% 500 250 50 9 out of 10 90 60 Can’t say < 5,00,000 100000 MXxJi1724688182ARM completed 2024-08-26 16:04:22 NaN 2024-08-26 16:13:02 ff51233502 6.018519
638 tod55nr27ozkyci5tod5rup87g8lo9ss Male 18 - 25 Diploma or vocation training Entrepreneur or Own Business 4 2 Somewhat Moderately Moderately Somewhat Moderately all or more than our household income, because... write down budget and spending, and have an em... 1 4 4 2 5 5 0 4 B B A Yes Yes Yes No No Irrespective of the quality of the study, futu... the growth rate will be 0.4% on average each year an answer is not possible based on the informa... The bank will default on 30% of repayments in ... 50% 70 in 100 people prior to the intervention to ... None of the above is implied. 500 10 1 59 out of 100 50 10 Hertinol 25,00,001 - 35,00,000 5000000 MXLz61724688143Vic completed 2024-08-26 16:04:07 NaN 2024-08-26 16:12:43 e7e6df96c7 5.972222
641 13nxdmew77mtq97scc6r13nxduhpev0l Male 25 - 35 Post Graduate Entrepreneur or Own Business 5 9 Moderately Moderately Moderately Moderately Somewhat less than half of the household income and sav... have an emergency fund, and spend as it feels ... 3 3 5 1 0 3 7 2 A B A Yes Yes Yes Yes No The higher the quality of the study, the more ... the growth rate will be 0.4% on average each year the stock price increased by 100% 30% of the banks customers will default next year 30% 100 in 10,000 people prior to the intervention... The medication increases recovery by 50% 500 100 1 59 out of 1000 25 60 Crosicol 15,00,001 - 25,00,000 13000000 MXcTq1724679467kFu completed 2024-08-26 13:39:40 NaN 2024-08-26 13:49:13 5a7cce9582 6.631944
642 s5m2omt6lyzdyeb1k9cs5m2omtmv05c3 Female 18 - 25 Post Graduate Employee/Consultant in Other than the Finance ... 6 1 Somewhat Moderately Moderately Moderately Moderately more than half of the household income and sav... write down budget and spending, and have an em... 5 0 6 1 6 6 0 6 A A A Yes Yes Yes Yes No The lower the quality of the study, the more l... it is not possible to predict the growth rate ... the stock price increased by 50% The bank will default on 30% of repayments in ... 30% 70 in 100 people prior to the intervention to ... The medication increases recovery by 50% 500 10 1 59 out of 1000 25 60 Crosicol 15,00,001 - 25,00,000 100000 MX4Er1724679488ZwW completed 2024-08-26 13:39:25 NaN 2024-08-26 13:54:38 3e73e75dfb 10.567130

489 rows × 53 columns

In [4]:
d0["Please state your current occupation."].value_counts()
Out[4]:
Please state your current occupation.
Employee/Consultant in Other than the Finance Sector    378
Entrepreneur or Own Business                             96
Student                                                   7
Home Maker or not employed                                5
Retired person                                            3
Name: count, dtype: int64

Demographics¶

In [5]:
# Age groups/rec = [2,3,4]
# Age group 2 = 18 to 35 y/o
# Age group 3 = 36 to 55 y/o
# Age group 4 = 56 to 75 y/o (75 y/o, i.e, within the scope of the data we have, it can mean 60 and above also)

d0["age_rec"] = np.where( d0["Please mark your age (in years)"] == "18 - 25" , 2,
                          np.where(d0["Please mark your age (in years)"] == "25 - 35", 2,
                                   np.where( d0["Please mark your age (in years)"] == "36 - 45", 3,
                                            np.where( d0["Please mark your age (in years)"] == "46 - 55", 3,
                                                     np.where( d0["Please mark your age (in years)"] == "56 - 65", 4,
                                                              np.where( d0["Please mark your age (in years)"] == "Above 65", 4, 0
                                                                      )
                                                             )
                                                    )
                                           )
                                  )
                        )

d0["age"] = np.where( d0["Please mark your age (in years)"] == "18 - 25" , (18 + 25) / 2,
                          np.where(d0["Please mark your age (in years)"] == "25 - 35", (26 + 35) / 2,
                                   np.where( d0["Please mark your age (in years)"] == "36 - 45", (36 + 45) / 2,
                                            np.where( d0["Please mark your age (in years)"] == "46 - 55", (46 + 55) / 2,
                                                     np.where( d0["Please mark your age (in years)"] == "56 - 65", (56 + 65) / 2,
                                                              np.where( d0["Please mark your age (in years)"] == "Above 65", (66 + 75) / 2, 0
                                                                      )
                                                             )
                                                    )
                                           )
                                  )
                        )


# Secondary upto 10 – ISCED 3
# Senior Secondary upto 12 - ISCED 3 
# Diploma and voca – ISCED 2
# UG – ISCED 2
# PG – ISCED 1
# PhD and higher – ISCED 1

d0["isced"] = np.where(d0["What is currently your highest Education?"] == "Post Graduate", 1,
                        np.where(d0["What is currently your highest Education?"] == "Under Graduate", 2,
                                 np.where(d0["What is currently your highest Education?"] == "Ph.D. or higher", 1,
                                          np.where(d0["What is currently your highest Education?"] == "Diploma or vocation training", 2,
                                                   np.where(d0["What is currently your highest Education?"] == "School degree (X or XII)", 3, 0
                                                           )
                                                  )
                                         )
                                )
                       )

d0["income"] = np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "< 5,00,000", 1,
                         np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "5,00,000 - 15,00,000", 2,
                                  np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "15,00,001 - 25,00,000", 3,
                                           np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "25,00,001 - 35,00,000", 4,
                                                    np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "35,00,001 - 45,00,000", 4,
                                                             np.where( d0["Please indicate your approximate annual personal income from all sources for last year"] == "> 45,00,000", 5,0
                                                                     )
                                                            )
                                                   )
                                           )
                                  )
                         )

d0["wealth"] = d0["Please provide a rough guess (in Indian Rupees) of the worth of your household's assets. Please do not forget to correct it for your debts, such as a mortgage or any loans you might have."]
In [ ]:
 

Questions¶

In [6]:
matchCol = ["q8_2_1", "q8_2_2", "q8_2_3", "q8_2_4", "q8_2_5", "q8_3", "q8_4", "q8_5berlin_1", 
            "q8_5london_1", "q8_5paris_1", "q8_6", "q8_7", "q9_1_1", "q9_2_1", "q9_3",
            "q10_1_1", "q10_2_1", "q10_3_1", "q10_4"]
d0[ matchCol ] = 0
In [7]:
d0.columns
Out[7]:
Index(['ResponseId', 'Please indicate your Gender.',
       'Please mark your age (in years)',
       'What is currently your highest Education?',
       'Please state your current occupation.',
       'How do you describe your willingness to take financial risk in general?',
       'Given the number of years that you have held various investments and the amount of investing you might have done, what degree of investment experience in the stock market do you have?',
       'If an expert tries to worry or scare me, i.e. a financial advisor about my financial situation, I choose another expert.',
       'I only buy a financial product I understand.', 'I trust doctors.',
       'When I want to buy a bigger item like a refrigerator or an expensive item of clothing, I wait a month to see whether I still want it and only buy it then.',
       'I always keep in mind that everything I do on the web could be used to my disadvantage.',
       'In my household, we/I spend:', 'In my household, we/I',
       '_Distribution 1_\n\nHow risky do you perceive the investment to be?',
       '_Distribution 2_\n\nHow risky do you perceive the investment to be?',
       '_Distribution 3_\n\nHow risky do you perceive the investment to be?',
       '_Distribution 4_\n\nHow risky do you perceive the investment to be?',
       '_Distribution 5_\n\nHow risky do you perceive the investment to be?',
       'Distribution 6\n\nHow risky do you perceive the investment to be?',
       'Distribution 7\n\nHow risky do you perceive the investment to be?',
       'Distribution 8\n\nHow risky do you perceive the investment to be?',
       '*Mumbai * A = 9 out of 10000   *OR   * B = 1 out of 1000',
       '*Bengaluru * A = 0.7%   *OR  * B = 0.099%',
       '*Kolkata * A = 0.61%   *OR   * B = 6 out of 10000', 'HIV test',
       'Fingerprint', 'DNA test', 'Cancer screening test',
       'Professional horoscope',
       'A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%.  Which of the following statements is true?',
       'There is an official prediction that the national stock market will grow 2% annually over the next 5 years.  This means that…',
       'Imagine you are told that the price of the stock Soya Ruchi increases from INR 60 to INR 120 after the company merger. What does this mean?',
       'It is predicted that Indigo Bank has 30% chance of default next year. Which of the following alternatives is the most appropriate interpretation of the statement?',
       'The probability that the economy will go into a recession this year is 30%. If the economy goes into recession, the probability that the stock market will decrease is 80%. If the economy does not go into a recession, the probability that the stock market will decrease is 23%. What is the probability that the economy goes into recession given that the stock market decreased?',
       'A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from:',
       'Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000.  This implies:',
       'Imagine that we flip a fair coin 1,000 times. What is your best guess about how many times the coin will come up heads in 1,000 flips? \n\n\_\_\_\_\_\_ times out of 1,000.',
       'In the Bingo Lottery, the chance of winning a $10 prize is 1%. What is your best guess about how many people will win a $10 prize if 1,000 people each buy a single ticket for Bingo Lottery?\n\n\_\_\_\_\_\_ person(s) out of 1,000.',
       'In a sweepstakes, the chance of winning a car is 1 in 1,000. What percentage of tickets for the sweepstakes wins a car?\n\n\_\_\_\_\_ % of tickets',
       'About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only]',
       'Approximately what percentage (%) of people who die from cancer die from colon cancer, breast cancer, and prostate cancer taken together?',
       'The following figure shows the number of men and women among a group of smartphone users. The total number of circles is 100. \n\nHow many more men than women are there among the 100 people using a smartphone?',
       'In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die?',
       'Please indicate your approximate annual personal income from all sources for last year',
       'Please provide a rough guess (in Indian Rupees) of the worth of your household's assets. Please do not forget to correct it for your debts, such as a mortgage or any loans you might have.',
       'uid', 'Response Type', 'Start Date (UTC)', 'Stage Date (UTC)',
       'Submit Date (UTC)', 'Network ID', 'LTA', 'age_rec', 'age', 'isced',
       'income', 'wealth', 'q8_2_1', 'q8_2_2', 'q8_2_3', 'q8_2_4', 'q8_2_5',
       'q8_3', 'q8_4', 'q8_5berlin_1', 'q8_5london_1', 'q8_5paris_1', 'q8_6',
       'q8_7', 'q9_1_1', 'q9_2_1', 'q9_3', 'q10_1_1', 'q10_2_1', 'q10_3_1',
       'q10_4'],
      dtype='object')
In [8]:
d0["HIV test"].value_counts()
Out[8]:
HIV test
Yes    340
No     149
Name: count, dtype: int64
In [9]:
d0["q8_2_1"] = np.where(d0["HIV test"] == "Yes", 1, 2)
d0["q8_2_2"] = np.where(d0["Fingerprint"] == "Yes", 1, 2)
d0["q8_2_3"] = np.where(d0["DNA test"] == "Yes", 1, 2)
d0["q8_2_4"] = np.where(d0["Cancer screening test"] == "Yes", 1, 2)
d0["q8_2_5"] = np.where(d0["Professional horoscope"] == "Yes", 1, 2)

d0["q8_3"] = np.where(d0["A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%.  Which of the following statements is true?"] == "The lower the quality of the study, the more likely that future studies will change the risk estimate.", 1,
                          np.where(d0["A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%.  Which of the following statements is true?"] == "The higher the quality of the study, the more likely that future studies will change the risk estimate.", 2,
                                  np.where(d0["A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%.  Which of the following statements is true?"] == "Irrespective of the quality of the study, future studies will not change the risk estimate.", 3,
                                          np.where(d0["A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%.  Which of the following statements is true?"] == "Irrespective of the quality of the study, future studies will change the risk estimate substantially anyway.", 4,0
                                                  )
                                          )
                                  )
                         )


d0["q8_4"] = np.where(d0["There is an official prediction that the national stock market will grow 2% annually over the next 5 years.  This means that…"] == "the growth rate will be 0.4% on average each year", 1,
                          np.where(d0["There is an official prediction that the national stock market will grow 2% annually over the next 5 years.  This means that…"] == "the growth rate over five years will be exactly 2%", 2,
                                  np.where(d0["There is an official prediction that the national stock market will grow 2% annually over the next 5 years.  This means that…"] == "the growth rate over five years will be between 1% and 3%", 3,
                                          np.where(d0["There is an official prediction that the national stock market will grow 2% annually over the next 5 years.  This means that…"] == "it is not possible to predict the growth rate with certainty.", 4,0
                                                  )
                                          )
                                  )
                         )
In [10]:
d0["q8_5berlin_1"] = np.where(d0["*Mumbai * A = 9 out of 10000   *OR   * B = 1 out of 1000"] == "A", 1, 2)
d0["q8_5london_1"] = np.where(d0["*Bengaluru * A = 0.7%   *OR  * B = 0.099%"] == "A", 1, 2)
d0["q8_5paris_1"] = np.where(d0["*Kolkata * A = 0.61%   *OR   * B = 6 out of 10000"] == "A", 1, 2)

d0["q8_6"] = np.where(d0["Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000.  This implies:"] == "The medication increases recovery by 100%", 1,
                          np.where(d0["Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000.  This implies:"] == "The medication increases recovery by 50%", 2,
                                   np.where(d0["Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000.  This implies:"] == "The medication increases recovery by 2%", 3,
                                            np.where(d0["Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000.  This implies:"] == "None of the above is implied.", 4, 0
                                                    )
                                           )
                                  )
                         )

d0["q8_7"] = np.where(d0["A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from:"] == "5 in 100 people prior to the intervention to 6 out of 100 people after the intervention", 1,
                          np.where(d0["A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from:"] == "100 in 10,000 people prior to the intervention to 120 out of 10,000 people after the intervention", 2,
                                   np.where(d0["A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from:"] == "70 in 100 people prior to the intervention to 90 out of 100 people after the intervention", 3,
                                            np.where(d0["A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from:"] == "it is not possible to determine which of the answers is correct given the information provided", 4, 0
                                                    )
                                           )
                                  )
                         )


d0["q9_1_1"] = d0["Approximately what percentage (%) of people who die from cancer die from colon cancer, breast cancer, and prostate cancer taken together?"].copy()
d0["q9_2_1"] = d0["The following figure shows the number of men and women among a group of smartphone users. The total number of circles is 100. \n\nHow many more men than women are there among the 100 people using a smartphone?"].copy()

d0["q9_3"] = np.where(d0["In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die?"] == "Crosicol", 1,
                          np.where(d0["In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die?"] == "Hertinol", 2,
                                   np.where(d0["In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die?"] == "They are equal", 3,
                                            np.where(d0["In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die?"] == "Can’t say", 4, 0
                                                    )
                                           )
                                  )
                         )
In [11]:
d0["q10_1_1"] = d0["Imagine that we flip a fair coin 1,000 times. What is your best guess about how many times the coin will come up heads in 1,000 flips? \n\n\_\_\_\_\_\_ times out of 1,000."].copy()
d0["q10_2_1"] = d0["In the Bingo Lottery, the chance of winning a $10 prize is 1%. What is your best guess about how many people will win a $10 prize if 1,000 people each buy a single ticket for Bingo Lottery?\n\n\_\_\_\_\_\_ person(s) out of 1,000."].copy()
d0["q10_3_1"] = d0["In a sweepstakes, the chance of winning a car is 1 in 1,000. What percentage of tickets for the sweepstakes wins a car?\n\n\_\_\_\_\_ % of tickets"].copy()

d0["q10_4"] = np.where(d0["About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only]"] == "9 out of 59", 1,
                          np.where(d0["About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only]"] == "9 out of 10", 2,
                                   np.where(d0["About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only]"] == "59 out of 1000", 3,
                                            np.where(d0["About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only]"] == "59 out of 100", 4, 0
                                                    )
                                           )
                                  )
                         )

Scoring¶

In [ ]:
 
In [12]:
scoreColumns = ["certainty1", "certainty2", "certainty3", "certainty4", "certainty5", "uncertainty1", "uncertainty2", "numeracy1", "numeracy2", "numeracy3", "numeracy4", "numeracy5", "graph1", "graph2", "graph3", "riskcomprehension1", "riskcomprehension2", "riskcomprehension3", "riskcomprehension4", "riskcomprehension5", "bayesian1"]
d0[scoreColumns] = 0
In [13]:
# Assigning scores

def scoring1(surveyFacet):
    surveyFacet.loc[ surveyFacet["q8_2_1"] == 2, "certainty1"] = 1
    surveyFacet.loc[ surveyFacet["q8_2_2"] == 2, "certainty2"] = 1
    surveyFacet.loc[ surveyFacet["q8_2_3"] == 2, "certainty3"] = 1
    surveyFacet.loc[ surveyFacet["q8_2_4"] == 2, "certainty4"] = 1
    surveyFacet.loc[ surveyFacet["q8_2_5"] == 2, "certainty5"] = 1

    surveyFacet.loc[ surveyFacet["q8_3"] == 1, "uncertainty1"] = 1
    surveyFacet.loc[ surveyFacet["q8_4"] == 4, "uncertainty2"] = 1

    surveyFacet.loc[ surveyFacet["q8_5berlin_1"] == 2, "riskcomprehension1"] = 1
    surveyFacet.loc[ surveyFacet["q8_5london_1"] == 1, "riskcomprehension2"] = 1
    surveyFacet.loc[ surveyFacet["q8_5paris_1"] == 1, "riskcomprehension3"] = 1
    
    surveyFacet.loc[ surveyFacet["q8_6"] == 1, "riskcomprehension4"] = 1
    surveyFacet.loc[ surveyFacet["q8_7"] == 4, "riskcomprehension5"] = 1

    surveyFacet.loc[ surveyFacet["q9_1_1"] == 25, "graph1"] = 1
    surveyFacet.loc[ surveyFacet["q9_2_1"] == 20, "graph2"] = 1
    surveyFacet.loc[ surveyFacet["q9_3"] == 3, "graph3"] = 1

    surveyFacet.loc[ surveyFacet["q10_1_1"] == 500, "numeracy1"] = 1
    surveyFacet.loc[ surveyFacet["q10_2_1"] == 10, "numeracy2"] = 1
    surveyFacet.loc[ (surveyFacet["q10_3_1"] == 0.1) | (surveyFacet["q10_3_1"] == ".1") | (surveyFacet["q10_3_1"] == ",1") , "numeracy3"] = 1
    surveyFacet.loc[ surveyFacet["q10_4"] == 1, "bayesian1"] = 1

    # Assigning total scores
    # surveyFacet["Certainty score_5"] = surveyFacet["certainty1"] + surveyFacet["certainty2"] + surveyFacet["certainty3"] + surveyFacet["certainty4"] + surveyFacet["certainty5"]
    #surveyFacet["Uncertainty score_5"] = surveyFacet["uncertainty1"] + surveyFacet["uncertainty2"]

    #surveyFacet["Number Comprehension score_5"] = surveyFacet["numeracy1"] + surveyFacet["numeracy2"] + surveyFacet["numeracy3"] + surveyFacet["numeracy4"] + surveyFacet["numeracy5"]
    #surveyFacet["Graph Comprehension score_5"] = surveyFacet["graph1"] + surveyFacet["graph2"] + surveyFacet["graph3"]

    #surveyFacet["Calculation score_4"] = surveyFacet["riskcalculation1"] + surveyFacet["riskcalculation2"] + surveyFacet["riskcalculation3"] + surveyFacet["riskcalculation4"]

    #surveyFacet["Total Score_19"] = surveyFacet["Certainty score_5"] + surveyFacet["Uncertainty score_5"] + surveyFacet["Number Comprehension score_5"] + surveyFacet["Graph Comprehension score_5"] + surveyFacet["Calculation score_4"]

    return surveyFacet
In [14]:
d0 = d0.groupby(["ResponseId"]).progress_apply(scoring1)
d0
100%|███████████████████████████████████████████████████████████████████████████████| 489/489 [00:02<00:00, 167.29it/s]
Out[14]:
ResponseId Please indicate your Gender. Please mark your age (in years) What is currently your highest Education? Please state your current occupation. How do you describe your willingness to take financial risk in general? Given the number of years that you have held various investments and the amount of investing you might have done, what degree of investment experience in the stock market do you have? If an expert tries to worry or scare me, i.e. a financial advisor about my financial situation, I choose another expert. I only buy a financial product I understand. I trust doctors. When I want to buy a bigger item like a refrigerator or an expensive item of clothing, I wait a month to see whether I still want it and only buy it then. I always keep in mind that everything I do on the web could be used to my disadvantage. In my household, we/I spend: In my household, we/I _Distribution 1_\n\nHow risky do you perceive the investment to be? _Distribution 2_\n\nHow risky do you perceive the investment to be? _Distribution 3_\n\nHow risky do you perceive the investment to be? _Distribution 4_\n\nHow risky do you perceive the investment to be? _Distribution 5_\n\nHow risky do you perceive the investment to be? Distribution 6\n\nHow risky do you perceive the investment to be? Distribution 7\n\nHow risky do you perceive the investment to be? Distribution 8\n\nHow risky do you perceive the investment to be? *Mumbai * A = 9 out of 10000 *OR * B = 1 out of 1000 *Bengaluru * A = 0.7% *OR * B = 0.099% *Kolkata * A = 0.61% *OR * B = 6 out of 10000 HIV test Fingerprint DNA test Cancer screening test Professional horoscope A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%. Which of the following statements is true? There is an official prediction that the national stock market will grow 2% annually over the next 5 years. This means that… Imagine you are told that the price of the stock Soya Ruchi increases from INR 60 to INR 120 after the company merger. What does this mean? It is predicted that Indigo Bank has 30% chance of default next year. Which of the following alternatives is the most appropriate interpretation of the statement? The probability that the economy will go into a recession this year is 30%. If the economy goes into recession, the probability that the stock market will decrease is 80%. If the economy does not go into a recession, the probability that the stock market will decrease is 23%. What is the probability that the economy goes into recession given that the stock market decreased? A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from: Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000. This implies: Imagine that we flip a fair coin 1,000 times. What is your best guess about how many times the coin will come up heads in 1,000 flips? \n\n\_\_\_\_\_\_ times out of 1,000. In the Bingo Lottery, the chance of winning a $10 prize is 1%. What is your best guess about how many people will win a $10 prize if 1,000 people each buy a single ticket for Bingo Lottery?\n\n\_\_\_\_\_\_ person(s) out of 1,000. In a sweepstakes, the chance of winning a car is 1 in 1,000. What percentage of tickets for the sweepstakes wins a car?\n\n\_\_\_\_\_ % of tickets About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only] Approximately what percentage (%) of people who die from cancer die from colon cancer, breast cancer, and prostate cancer taken together? The following figure shows the number of men and women among a group of smartphone users. The total number of circles is 100. \n\nHow many more men than women are there among the 100 people using a smartphone? In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die? Please indicate your approximate annual personal income from all sources for last year Please provide a rough guess (in Indian Rupees) of the worth of your household's assets. Please do not forget to correct it for your debts, such as a mortgage or any loans you might have. uid Response Type Start Date (UTC) Stage Date (UTC) Submit Date (UTC) Network ID LTA age_rec age isced income wealth q8_2_1 q8_2_2 q8_2_3 q8_2_4 q8_2_5 q8_3 q8_4 q8_5berlin_1 q8_5london_1 q8_5paris_1 q8_6 q8_7 q9_1_1 q9_2_1 q9_3 q10_1_1 q10_2_1 q10_3_1 q10_4 certainty1 certainty2 certainty3 certainty4 certainty5 uncertainty1 uncertainty2 numeracy1 numeracy2 numeracy3 numeracy4 numeracy5 graph1 graph2 graph3 riskcomprehension1 riskcomprehension2 riskcomprehension3 riskcomprehension4 riskcomprehension5 bayesian1
ResponseId
00ujdxbfoya0donu8r00ujcjdkojc99x 428 00ujdxbfoya0donu8r00ujcjdkojc99x Female 25 - 35 Under Graduate Employee/Consultant in Other than the Finance ... 7 8 Completely Completely Completely Moderately Moderately less than half of the household income and sav... write down budget and spending, and have an em... 2 1 1 1 0 2 0 0 B B A Yes Yes Yes Yes No The higher the quality of the study, the more ... the growth rate over five years will be betwee... the stock price increased by 50% The bank will default on 30% of repayments in ... 50% 100 in 10,000 people prior to the intervention... The medication increases recovery by 50% 500 100 1 59 out of 1000 50 10 They are equal &lt; 5,00,000 1500000 MXic21725178744djB completed 2024-09-01 08:23:04 NaN 2024-09-01 09:24:44 1c1d51349c 42.824074 2 30.5 2 1 1500000 1 1 1 1 2 2 3 2 2 1 2 2 50 10 3 500 100 1 3 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 1 0 0 0
01ro2iftnum5v4cn01r03c75aa24emhq 528 01ro2iftnum5v4cn01r03c75aa24emhq Female 18 - 25 Under Graduate Student 5 8 Just about Completely Completely Completely Just about more than half of the household income and sav... write down budget and spending, and have an em... 6 6 2 6 6 6 2 4 A B B Yes Yes No Yes Yes The lower the quality of the study, the more l... the growth rate over five years will be exactl... an answer is not possible based on the informa... Banks similar to Indigo will default 30% of th... 50% 70 in 100 people prior to the intervention to ... The medication increases recovery by 100% 46 100 99 59 out of 1000 79 40 Hertinol 5,00,000 - 15,00,000 500000 MX7wI1724844822zNm completed 2024-08-28 11:36:50 NaN 2024-08-28 11:51:07 81a8a6836a 9.918981 2 21.5 2 2 500000 1 1 2 1 1 1 2 1 2 2 1 3 79 40 2 46 100 99 3 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
022xoawhrghfhv4a1g022xmz7hb0m41f 442 022xoawhrghfhv4a1g022xmz7hb0m41f Female 46 - 55 Under Graduate Entrepreneur or Own Business 5 4 Somewhat Somewhat Moderately Just about Somewhat all or more than the household income, even th... spend as it feel right, and do not have an eme... 4 4 4 4 4 4 4 4 B A B Yes Yes Yes No No The lower the quality of the study, the more l... the growth rate will be 0.4% on average each year the stock price increased by 60% 30% of the banks customers will default next year 80% 5 in 100 people prior to the intervention to 6... The medication increases recovery by 2% 500 1 1 59 out of 1000 17 65 They are equal &lt; 5,00,000 15000000 MXFDH1725178659xRn completed 2024-09-01 08:21:14 NaN 2024-09-01 08:33:49 fbd8962401 8.738426 3 50.5 2 1 15000000 1 1 1 2 2 1 1 2 1 2 3 1 17 65 3 500 1 1 3 0 0 0 1 1 1 0 1 0 0 0 0 0 0 1 1 1 0 0 0 0
02pxtdbyibecqqfvwlw02pxwfbane9zd 558 02pxtdbyibecqqfvwlw02pxwfbane9zd Female 46 - 55 Post Graduate Employee/Consultant in Other than the Finance ... 6 10 Not at all Completely Completely Completely Moderately all or more than the household income, even th... have an emergency fund, and spend as it feels ... 5 5 6 5 7 5 0 5 B A A Yes Yes Yes No Yes The higher the quality of the study, the more ... it is not possible to predict the growth rate ... an answer is not possible based on the informa... 30% of the banks customers will default next year 30% 5 in 100 people prior to the intervention to 6... None of the above is implied. 1000 1 100 9 out of 10 20 20 Can’t say 5,00,000 - 15,00,000 3000000 MXIpM1724844742xet completed 2024-08-28 11:35:05 NaN 2024-08-28 11:48:47 e8b668d11b 9.513889 3 50.5 1 2 3000000 1 1 1 2 1 2 4 2 1 1 4 1 20 20 4 1000 1 100 2 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 1 1 1 0 0 0
037aefjdt26mnjd102nz0pk037aedfhc 544 037aefjdt26mnjd102nz0pk037aedfhc Male 46 - 55 Post Graduate Employee/Consultant in Other than the Finance ... 3 6 Somewhat Somewhat Somewhat Somewhat Somewhat more than half of the household income and sav... write down budget and spending, but have no em... 3 1 6 1 2 6 7 5 A B A No No Yes No No Irrespective of the quality of the study, futu... it is not possible to predict the growth rate ... an answer is not possible based on the informa... Banks similar to Indigo will default 30% of th... 60% 70 in 100 people prior to the intervention to ... None of the above is implied. 1000 1 10 9 out of 59 50 20 Hertinol 5,00,000 - 15,00,000 7000000 MXpv61724844846UVs completed 2024-08-28 11:35:50 NaN 2024-08-28 11:45:54 f51b723011 6.990741 3 50.5 1 2 7000000 2 2 1 2 2 3 4 1 2 1 4 3 50 20 2 1000 1 10 1 1 1 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
zcj7ldokhyo6217f9sriwizcj7ldodwt 268 zcj7ldokhyo6217f9sriwizcj7ldodwt Female 36 - 45 Under Graduate Entrepreneur or Own Business 5 8 Completely Completely Completely Not at all Moderately less than half of the household income and sav... write down budget and spending, and have an em... 2 0 7 5 5 7 0 4 A B A No Yes Yes No No The higher the quality of the study, the more ... it is not possible to predict the growth rate ... the stock price increased by 100% Banks similar to Indigo will default 30% of th... 80% it is not possible to determine which of the a... The medication increases recovery by 2% 500 10 1 9 out of 59 25 60 Hertinol 15,00,001 - 25,00,000 600000000 MX6PC1725259690mDV completed 2024-09-02 06:53:29 NaN 2024-09-02 07:28:48 237c2e5291 24.525463 3 40.5 2 3 600000000 2 1 1 2 2 2 4 1 2 1 3 4 25 60 2 500 10 1 1 1 0 0 1 1 0 1 1 1 0 0 0 1 0 0 0 0 1 0 1 1
zfemo30rg0ekva18x1kjwzfemo2wpg2p 185 zfemo30rg0ekva18x1kjwzfemo2wpg2p Male 36 - 45 Ph.D. or higher Employee/Consultant in Other than the Finance ... 6 8 Not at all Moderately Just about Not at all Not at all less than half of the household income and sav... have an emergency fund, and spend as it feels ... 3 1 4 2 0 5 0 2 B A A No Yes Yes No No Irrespective of the quality of the study, futu... the growth rate over five years will be betwee... the stock price increased by 100% 30% of central bankers think that Indigo Bank ... 30% 100 in 10,000 people prior to the intervention... The medication increases recovery by 100% 500 10 1 59 out of 1000 25 20 Can’t say 15,00,001 - 25,00,000 8500000 MXUxt1725260651heG completed 2024-09-02 07:08:18 NaN 2024-09-02 07:23:09 fbb956cffc 10.312500 3 40.5 1 3 8500000 2 1 1 2 2 4 3 2 1 1 1 2 25 20 4 500 10 1 3 1 0 0 1 1 0 0 1 1 0 0 0 1 1 0 1 1 1 1 0 0
znqvw4t38br3072znqvwvjsgb6wvj7nt 305 znqvw4t38br3072znqvwvjsgb6wvj7nt Female 36 - 45 Post Graduate Entrepreneur or Own Business 5 5 Somewhat Completely Just about Moderately Somewhat less than half of the household income and sav... spend as it feel right, and do not have an eme... 0 0 0 1 0 0 0 0 B B B Yes Yes Yes No No Irrespective of the quality of the study, futu... it is not possible to predict the growth rate ... the stock price increased by 50% 30% of the banks customers will default next year 80% it is not possible to determine which of the a... The medication increases recovery by 2% 500 10 1 9 out of 59 25 20 Hertinol 5,00,000 - 15,00,000 2000000 MXd921725259652XDJ completed 2024-09-02 06:49:56 NaN 2024-09-02 07:17:20 1964f49e1c 19.027778 3 40.5 1 2 2000000 1 1 1 2 2 3 4 2 2 2 3 4 25 20 2 500 10 1 1 0 0 0 1 1 0 1 1 1 0 0 0 1 1 0 1 0 0 0 1 1
zstpq23h1x3ilab8s8vsrbkw8t4zstpq 306 zstpq23h1x3ilab8s8vsrbkw8t4zstpq Male 18 - 25 Under Graduate Entrepreneur or Own Business 6 9 Completely Completely Moderately Completely Moderately all or more than our household income, because... have an emergency fund, and spend as it feels ... 6 3 7 1 2 7 7 3 A A B No Yes Yes No No The higher the quality of the study, the more ... the growth rate over five years will be betwee... the stock price increased by 60% Banks similar to Indigo will default 30% of th... 80% 5 in 100 people prior to the intervention to 6... None of the above is implied. 750 10 1 59 out of 1000 50 10 They are equal 5,00,000 - 15,00,000 60000000 MXfmg1725259705Fkj completed 2024-09-02 06:49:52 NaN 2024-09-02 07:01:04 03aa77ec48 7.777778 2 21.5 2 2 60000000 2 1 1 2 2 2 3 1 1 2 4 1 50 10 3 750 10 1 3 1 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0
ztfdlcbiyo1xco3ztfdl4ozqpte2q3m9 95 ztfdlcbiyo1xco3ztfdl4ozqpte2q3m9 Male 25 - 35 Post Graduate Entrepreneur or Own Business 6 7 Somewhat Just about Completely Moderately Somewhat more than half of the household income and sav... spend as it feel right, and do not have an eme... 6 0 2 5 3 7 0 4 B A A No Yes Yes Yes Yes The higher the quality of the study, the more ... it is not possible to predict the growth rate ... the stock price increased by 100% 30% of central bankers think that Indigo Bank ... 80% 5 in 100 people prior to the intervention to 6... The medication increases recovery by 2% 500 10 1 59 out of 1000 25 60 Can’t say 15,00,001 - 25,00,000 5000000 MXjp51725955476hMt completed 2024-09-10 08:06:34 NaN 2024-09-10 08:16:35 f68294192e 6.956019 2 30.5 1 3 5000000 2 1 1 1 1 2 4 2 1 1 3 1 25 60 4 500 10 1 3 1 0 0 0 0 0 1 1 1 0 0 0 1 0 0 1 1 1 0 0 0

489 rows × 98 columns

In [ ]:
 
In [15]:
def scoring2(survey):
    
    survey["Certainty_5"] = survey["certainty1"] + survey["certainty2"] + survey["certainty3"] + survey["certainty4"] + survey["certainty5"]
    
    survey["Uncertainty_2"] = survey["uncertainty1"] + survey["uncertainty2"]
    
    survey["RiskComprehension_5"] = survey["riskcomprehension1"] + survey["riskcomprehension2"] + survey["riskcomprehension3"] + survey["riskcomprehension4"] + survey["riskcomprehension5"]
    
    survey["GraphLiteracy_3"] = survey["graph1"] + survey["graph2"] + survey["graph3"]
    
    survey["Numeracy_2"] = survey["numeracy1"] + survey["numeracy2"] # + survey["numeracy3"]
    
    survey["Bayesianreasoning_1"] = survey["bayesian1"]
    
    survey["TotalScore_18"] = survey["Certainty_5"] + survey["Uncertainty_2"] + survey["RiskComprehension_5"] + survey["GraphLiteracy_3"] + survey["Numeracy_2"] + survey["Bayesianreasoning_1"]
    
    survey["Certainty_%"] = survey["Certainty_5"] / 5 * 100
    survey["Uncertainty_%"] = survey["Uncertainty_2"] / 2 * 100
    survey["RiskComprehension_%"] = survey["RiskComprehension_5"] / 5 * 100
    survey["GraphLiteracy_%"] = survey["GraphLiteracy_3"] / 3 * 100
    survey["Numeracy_%"] = survey["Numeracy_2"] / 2 * 100
    survey["Bayesianreasoning_%"] = survey["Bayesianreasoning_1"] / 1 * 100
    
    survey["TotalScore_%"] = survey["TotalScore_18"] / 18 * 100
    
    
    colReq = ["ResponseId", "age", "age_rec", "isced", "income", "wealth", "Certainty_5", "Uncertainty_2", "RiskComprehension_5", "GraphLiteracy_3",
              "Numeracy_2", "Bayesianreasoning_1", "Certainty_%", "Uncertainty_%", "RiskComprehension_%", "GraphLiteracy_%", "Numeracy_%", "Bayesianreasoning_%",
              "TotalScore_18", "TotalScore_%",]
    
    survey1 = survey[colReq].copy()
    return survey1
In [16]:
d01 = scoring2(d0)
d01
Out[16]:
ResponseId age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_%
ResponseId
00ujdxbfoya0donu8r00ujcjdkojc99x 428 00ujdxbfoya0donu8r00ujcjdkojc99x 30.5 2 2 1 1500000 1 0 2 1 1 0 20.0 0.0 40.0 33.333333 50.0 0.0 5 27.777778
01ro2iftnum5v4cn01r03c75aa24emhq 528 01ro2iftnum5v4cn01r03c75aa24emhq 21.5 2 2 2 500000 1 1 1 0 0 0 20.0 50.0 20.0 0.000000 0.0 0.0 3 16.666667
022xoawhrghfhv4a1g022xmz7hb0m41f 442 022xoawhrghfhv4a1g022xmz7hb0m41f 50.5 3 2 1 15000000 2 1 2 1 1 0 40.0 50.0 40.0 33.333333 50.0 0.0 7 38.888889
02pxtdbyibecqqfvwlw02pxwfbane9zd 558 02pxtdbyibecqqfvwlw02pxwfbane9zd 50.5 3 1 2 3000000 1 1 3 1 0 0 20.0 50.0 60.0 33.333333 0.0 0.0 6 33.333333
037aefjdt26mnjd102nz0pk037aedfhc 544 037aefjdt26mnjd102nz0pk037aedfhc 50.5 3 1 2 7000000 4 1 1 1 0 1 80.0 50.0 20.0 33.333333 0.0 100.0 8 44.444444
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
zcj7ldokhyo6217f9sriwizcj7ldodwt 268 zcj7ldokhyo6217f9sriwizcj7ldodwt 40.5 3 2 3 600000000 3 1 2 1 2 1 60.0 50.0 40.0 33.333333 100.0 100.0 10 55.555556
zfemo30rg0ekva18x1kjwzfemo2wpg2p 185 zfemo30rg0ekva18x1kjwzfemo2wpg2p 40.5 3 1 3 8500000 3 0 4 2 2 0 60.0 0.0 80.0 66.666667 100.0 0.0 11 61.111111
znqvw4t38br3072znqvwvjsgb6wvj7nt 305 znqvw4t38br3072znqvwvjsgb6wvj7nt 40.5 3 1 2 2000000 2 1 2 2 2 1 40.0 50.0 40.0 66.666667 100.0 100.0 10 55.555556
zstpq23h1x3ilab8s8vsrbkw8t4zstpq 306 zstpq23h1x3ilab8s8vsrbkw8t4zstpq 21.5 2 2 2 60000000 3 0 1 1 1 0 60.0 0.0 20.0 33.333333 50.0 0.0 6 33.333333
ztfdlcbiyo1xco3ztfdl4ozqpte2q3m9 95 ztfdlcbiyo1xco3ztfdl4ozqpte2q3m9 30.5 2 1 3 5000000 1 1 3 1 2 0 20.0 50.0 60.0 33.333333 100.0 0.0 8 44.444444

489 rows × 20 columns

In [ ]:
 
In [ ]:
 

BeSample Data¶

In [17]:
d1 = pd.read_csv('Indian Risk Survey_Besample_Filtered_12_2024.csv')
d11 = d1.iloc[:, 19:62].copy()
d11.insert(0, "ResponseId",0)
d11["ResponseId"] = d1["ResponseId"].copy()
d11 = d11.loc[ (d11["Q4"] != "Stock analyst") & (d11["Q4"] != "Insurance agent, etc") & (d11["Q4"] != "Venture capital/private equity")]
In [18]:
d11["Q4"].value_counts()
Out[18]:
Q4
Salaried/Employee/Consultant in a sector other than Finance    167
Student                                                        100
Not employed                                                    50
Entrepreneur/Business Owner in a sector other than Finance      30
Retired                                                          3
Name: count, dtype: int64
In [19]:
d11["age"] = d11["Q2"]

# Age groups/rec = [2,3,4]
# Age group 2 = 18 to 35 y/o
# Age group 3 = 36 to 55 y/o
# Age group 4 = 56 to 75 y/o (75 y/o, i.e, within the scope of the data we have, it can mean 60 and above also)

d11["age_rec"] = np.where( (d11["age"] >= 18) & (d11["age"] <= 35), 2,
                          np.where( (d11["age"] >= 36) & (d11["age"] <= 55), 3,
                                   np.where( (d11["age"] >= 56), 4, 0
                                           )
                                  )
                         )


# Secondary upto 10 – ISCED 3
# Senior Secondary upto 12 - ISCED 3 
# Diploma and voca – ISCED 2
# UG – ISCED 2
# PG – ISCED 1
# PhD and higher – ISCED 1

d11["isced"] = np.where(d11["Q3"] == "Post-Graduate Program", 1,
                        np.where(d11["Q3"] == "Undergraduate Program", 2,
                                 np.where(d11["Q3"] == "Ph.D. and higher", 1,
                                          np.where(d11["Q3"] == "Diploma and Vocational Training", 2,
                                                   np.where(d11["Q3"] == "Secondary School (11th to 12th Std.)", 3,
                                                            np.where( d11["Q3"] == "Primary School (up to 10th Std.)", 3,
                                                                     np.where( d11["Q3"] == "M.Phil.", 1, 0
                                                                             )
                                                                    )
                                                           )
                                                  )
                                         )
                                )
                       )

d11["income"] = np.where(d11["Q15a"] == "< INR 500,000", 1,
                         np.where(d11["Q15a"] == "INR 500,001 – INR 15,00,000", 2,
                                  np.where(d11["Q15a"] == "INR 1500,001 – INR 30,00,000", 3,
                                           np.where(d11["Q15a"] == "INR 30,00,001 – INR 50,00,000", 4,
                                                    np.where(d11["Q15a"] == "INR 50,00,001 – INR 75,00,000", 5,
                                                             np.where( d11["Q15a"] == "> INR 75,00,000", 5,0
                                                                     )
                                                            )
                                                   )
                                           )
                                  )
                         )

d11["wealth"] = d11["Q14b.1"]

Scoring¶

In [20]:
matchCol = ["q8_2_1", "q8_2_2", "q8_2_3", "q8_2_4", "q8_2_5", "q8_3", "q8_4", "q8_5berlin_1", 
            "q8_5london_1", "q8_5paris_1", "q8_6", "q8_7", "q9_1_1", "q9_2_1", "q9_3",
            "q10_1_1", "q10_2_1", "q10_3_1", "q10_4"]
d11[ matchCol ] = 0

d11.columns
Out[20]:
Index(['ResponseId', 'Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5_1', 'Q6_1', 'Q8_1',
       'Q8_2', 'Q8_3', 'Q8_4', 'Q8_5', 'Q8_6', 'Q8_7', 'Q8_8', 'Q9b_1',
       'Q9b_4', 'Q9b_5', 'Q9b_6', 'Q9b_7', 'Q9b_8', 'Q9b_9', 'Q11a_1',
       'Q11a_2', 'Q11a_3', 'Q11b', 'Q11c', 'Q11d', 'Q11h', 'Attention Check',
       'Q11i', 'Q12a', 'Q12b', 'Q12c', 'Q13a', 'Q13b', 'Q13c', 'Q13d', 'Q14a',
       'Q14b', 'Q14c', 'Q15a', 'Q14b.1', 'age', 'age_rec', 'isced', 'income',
       'wealth', 'q8_2_1', 'q8_2_2', 'q8_2_3', 'q8_2_4', 'q8_2_5', 'q8_3',
       'q8_4', 'q8_5berlin_1', 'q8_5london_1', 'q8_5paris_1', 'q8_6', 'q8_7',
       'q9_1_1', 'q9_2_1', 'q9_3', 'q10_1_1', 'q10_2_1', 'q10_3_1', 'q10_4'],
      dtype='object')
In [21]:
d11["q8_2_1"] = np.where(d11["Q11b"].str.contains("HIV test"), 1, 2)
d11["q8_2_2"] = np.where(d11["Q11b"].str.contains("Fingerprint"), 1, 2)
d11["q8_2_3"] = np.where(d11["Q11b"].str.contains("DNA test"), 1, 2)
d11["q8_2_4"] = np.where(d11["Q11b"].str.contains("Cancer screening test"), 1, 2)
d11["q8_2_5"] = np.where(d11["Q11b"].str.contains("Professional horoscope"), 1, 2)

d11["q8_3"] = np.where(d11["Q11c"] == "The lower the quality of the study, the more likely that future studies will change the risk estimate.", 1,
                          np.where(d11["Q11c"] == "The higher the quality of the study, the more likely that future studies will change the risk estimate.", 2,
                                  np.where(d11["Q11c"] == "Irrespective of the quality of the study, future studies will not change the risk estimate.", 3,
                                          np.where(d11["Q11c"] == "Irrespective of the quality of the study, future studies will change the risk estimate substantially anyway.", 4,0
                                                  )
                                          )
                                  )
                         )


d11["q8_4"] = np.where(d11["Q11d"] == "The growth rate will be 0.4% on average each year", 1,
                          np.where(d11["Q11d"] == "The growth rate over five years will be exactly 2%", 2,
                                  np.where(d11["Q11d"] == "The growth rate over five years will be between 1% and 3%", 3,
                                          np.where(d11["Q11d"] == "It is not possible to predict the growth rate with certainty", 4,0
                                                  )
                                          )
                                  )
                         )
In [22]:
d11["q8_5berlin_1"] = np.where(d11["Q11a_1"] == 1, 1, 2)
d11["q8_5london_1"] = np.where(d11["Q11a_1"] == 1, 1, 2)
d11["q8_5paris_1"] = np.where(d11["Q11a_1"] == 1, 1, 2)

d11["q8_6"] = np.where(d11["Q11i"] == "The medication increases recovery by 100%", 1,
                          np.where(d11["Q11i"] == "The medication increases recovery by 50%", 2,
                                   np.where(d11["Q11i"] == "The medication increases recovery by 2%", 3,
                                            np.where(d11["Q11i"] == "None of the above is implied", 4, 0
                                                    )
                                           )
                                  )
                         )

d11["q8_7"] = np.where(d11["Q11h"] == "5 in 100 people prior to the intervention to 6 out of 100 people after the intervention", 1,
                          np.where(d11["Q11h"] == "100 in 10,000 people prior to the intervention to 120 out of 10,000 people after the intervention", 2,
                                   np.where(d11["Q11h"] == "70 in 100 people prior to the intervention to 90 out of 100 people after the intervention", 3,
                                            np.where(d11["Q11h"] == "It is not possible to determine which of the answers is correct given the information provided", 4, 0
                                                    )
                                           )
                                  )
                         )


d11["q9_1_1"] = d11["Q14a"].copy()
d11["q9_2_1"] = d11["Q14b"].copy()

d11["q9_3"] = np.where(d11["Q14c"] == "Crosicol", 1,
                          np.where(d11["Q14c"] == "Hertinol", 2,
                                   np.where(d11["Q14c"] == "They are equal", 3,
                                            np.where(d11["Q14c"] == "Can’t say", 4, 0
                                                    )
                                           )
                                  )
                         )
In [23]:
d11["q10_1_1"] = d11["Q13a"].copy()
d11["q10_2_1"] = d11["Q13b"].copy()
d11["q10_3_1"] = d11["Q13c"].copy()

d11["q10_4"] = np.where(d11["Q13d"] == "9 out of 59", 1,
                          np.where(d11["Q13d"] == "9 out of 10", 2,
                                   np.where(d11["Q13d"] == "59 out of 1000", 3,
                                            np.where(d11["Q13d"] == "59 out of 100", 4, 0
                                                    )
                                           )
                                  )
                         )
In [24]:
d11[ matchCol ]
Out[24]:
q8_2_1 q8_2_2 q8_2_3 q8_2_4 q8_2_5 q8_3 q8_4 q8_5berlin_1 q8_5london_1 q8_5paris_1 q8_6 q8_7 q9_1_1 q9_2_1 q9_3 q10_1_1 q10_2_1 q10_3_1 q10_4
0 1 2 2 2 1 4 3 2 2 2 1 1 25.0 20 3 500 10 0.10 2
1 1 1 1 2 2 4 4 2 2 2 1 2 25.0 20 4 500 10 0.10 3
2 2 1 2 2 2 2 4 1 1 1 2 3 50.0 20 3 1000 10 50.00 1
3 2 1 1 2 2 2 2 1 1 1 2 2 25.0 20 3 500 10 0.10 3
4 1 1 1 1 2 2 2 2 2 2 1 2 25.0 20 3 500 10 0.10 3
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
365 2 2 2 2 1 3 2 1 1 1 3 3 50.0 40 3 500 68 0.01 4
366 2 2 2 1 2 2 2 2 2 2 3 4 25.0 20 3 500 50 0.10 1
367 2 2 1 2 2 3 4 1 1 1 3 4 90.0 20 3 345 1 0.10 2
368 1 1 1 1 2 2 3 2 2 2 1 3 25.0 5 2 600 10 0.10 2
369 2 2 1 2 2 2 4 2 2 2 1 1 25.0 10 2 500 500 50.00 3

350 rows × 19 columns

In [25]:
scoreColumns = ["certainty1", "certainty2", "certainty3", "certainty4", "certainty5", "uncertainty1", "uncertainty2", "numeracy1", "numeracy2", "numeracy3", "numeracy4", "numeracy5", "graph1", "graph2", "graph3", "riskcomprehension1", "riskcomprehension2", "riskcomprehension3", "riskcomprehension4", "riskcomprehension5", "bayesian1"]
d11[scoreColumns] = 0
In [26]:
d11Facet = d11.groupby(["ResponseId"]).progress_apply(scoring1)
100%|███████████████████████████████████████████████████████████████████████████████| 350/350 [00:02<00:00, 155.06it/s]
In [27]:
d11Facet
Out[27]:
ResponseId Q0 Q1 Q2 Q3 Q4 Q5_1 Q6_1 Q8_1 Q8_2 Q8_3 Q8_4 Q8_5 Q8_6 Q8_7 Q8_8 Q9b_1 Q9b_4 Q9b_5 Q9b_6 Q9b_7 Q9b_8 Q9b_9 Q11a_1 Q11a_2 Q11a_3 Q11b Q11c Q11d Q11h Attention Check Q11i Q12a Q12b Q12c Q13a Q13b Q13c Q13d Q14a Q14b Q14c Q15a Q14b.1 age age_rec isced income wealth q8_2_1 q8_2_2 q8_2_3 q8_2_4 q8_2_5 q8_3 q8_4 q8_5berlin_1 q8_5london_1 q8_5paris_1 q8_6 q8_7 q9_1_1 q9_2_1 q9_3 q10_1_1 q10_2_1 q10_3_1 q10_4 certainty1 certainty2 certainty3 certainty4 certainty5 uncertainty1 uncertainty2 numeracy1 numeracy2 numeracy3 numeracy4 numeracy5 graph1 graph2 graph3 riskcomprehension1 riskcomprehension2 riskcomprehension3 riskcomprehension4 riskcomprehension5 bayesian1
ResponseId
R_1YkMM2lMB9aEuVL 150 R_1YkMM2lMB9aEuVL Yes, I would like to participate in the study ... Female 41 Undergraduate Program Salaried/Employee/Consultant in a sector other... 3 3 3 4 3 3 3 3 3 4 85.0 0.0 0 0 5.0 10 0.0 1 1 1 DNA test The higher the quality of the study, the more ... The growth rate over five years will be betwee... 5 in 100 people prior to the intervention to 6... Vase The medication increases recovery by 50% Less than $102 Exactly the same as today with the money in th... True 1000 10 1.0 59 out of 1000 9.0 20 Crosicol < INR 500,000 25000.0 41 3 2 1 25000.0 2 2 1 2 2 2 3 1 1 1 2 1 9.0 20 1 1000 10 1.0 3 1 1 0 1 1 0 0 0 1 0 0 0 0 1 0 0 1 1 0 0 0
R_40N8bUn3C0jwGzf 200 R_40N8bUn3C0jwGzf Yes, I would like to participate in the study ... Female 18 Undergraduate Program Student 0 (unwilling to take risk) 0 (no investment experience) 3 3 4 3 3 3 2 3 40.0 20.0 10 10 10.0 5 5.0 2 1 1 HIV test,Fingerprint,DNA test,Cancer screening... The higher the quality of the study, the more ... The growth rate over five years will be betwee... It is not possible to determine which of the a... Vase The medication increases recovery by 2% More than $102 More than today with the money in this account True 1000 500 100.0 9 out of 59 11.0 60 They are equal < INR 500,000 80000.0 18 2 2 1 80000.0 1 1 1 1 2 2 3 2 2 2 3 4 11.0 60 3 1000 500 100.0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1
R_40VyTmJ6i96wUP4 235 R_40VyTmJ6i96wUP4 Yes, I would like to participate in the study ... Female 34 Post-Graduate Program Not employed 2 0 (no investment experience) 3 2 2 3 3 3 2 3 40.0 25.0 10 10 15.0 0 0.0 1 2 1 DNA test The higher the quality of the study, the more ... It is not possible to predict the growth rate ... It is not possible to determine which of the a... Vase The medication increases recovery by 2% Refuse to answer Less than today with the money in this account Do not know 698 352 1.0 59 out of 1000 30.0 20 Crosicol < INR 500,000 500000.0 34 2 1 1 500000.0 2 2 1 2 2 2 4 1 1 1 3 4 30.0 20 1 698 352 1.0 3 1 1 0 1 1 0 1 0 0 0 0 0 0 1 0 0 1 1 0 1 0
R_40ZEg7vX3Y7mMQl 184 R_40ZEg7vX3Y7mMQl Yes, I would like to participate in the study ... Female 31 Undergraduate Program Not employed 4 6 2 2 2 1 (strongly disagree) 2 3 4 1 (strongly disagree) 30.0 30.0 10 0 20.0 10 0.0 1 1 1 HIV test,Fingerprint,DNA test,Cancer screening... The higher the quality of the study, the more ... The growth rate will be 0.4% on average each year 70 in 100 people prior to the intervention to ... Vase The medication increases recovery by 2% More than $102 Less than today with the money in this account True 500 10 10.0 59 out of 1000 50.0 20 They are equal < INR 500,000 500000.0 31 2 2 1 500000.0 1 1 1 1 2 2 1 1 1 1 3 3 50.0 20 3 500 10 10.0 3 0 0 0 0 1 0 0 1 1 0 0 0 0 1 1 0 1 1 0 0 0
R_40cbsHWzTyKyFxv 53 R_40cbsHWzTyKyFxv Yes, I would like to participate in the study ... Male 38 Undergraduate Program Salaried/Employee/Consultant in a sector other... 5 5 4 6 (strongly agree) 5 4 6 (strongly agree) 6 (strongly agree) 6 (strongly agree) 6 (strongly agree) 45.0 35.0 0 5 15.0 0 0.0 2 1 2 Fingerprint The higher the quality of the study, the more ... The growth rate over five years will be exactl... It is not possible to determine which of the a... Vase The medication increases recovery by 2% More than $102 Exactly the same as today with the money in th... False 1000 100 25.0 9 out of 10 30.0 20 They are equal < INR 500,000 300000.0 38 3 2 1 300000.0 2 1 2 2 2 2 2 2 2 2 3 4 30.0 20 3 1000 100 25.0 2 1 0 1 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
R_4yzFrFYMa7w8nQZ 238 R_4yzFrFYMa7w8nQZ Yes, I would like to participate in the study ... Female 18 Secondary School (11th to 12th Std.) Student 0 (unwilling to take risk) 0 (no investment experience) 6 (strongly agree) 5 5 5 4 4 5 6 (strongly agree) 20.0 10.0 15 25 5.0 9 16.0 1 2 1 DNA test The higher the quality of the study, the more ... The growth rate over five years will be exactl... 70 in 100 people prior to the intervention to ... Vase The medication increases recovery by 100% More than $102 Less than today with the money in this account True 1000 100 100.0 59 out of 1000 25.0 20 Hertinol < INR 500,000 90000.0 18 2 3 1 90000.0 2 2 1 2 2 2 2 1 1 1 1 3 25.0 20 2 1000 100 100.0 3 1 1 0 1 1 0 0 0 0 0 0 0 1 1 0 0 1 1 1 0 0
R_4zPiSk23ayGvFD4 364 R_4zPiSk23ayGvFD4 Yes, I would like to participate in the study ... Male 70 Undergraduate Program Salaried/Employee/Consultant in a sector other... 4 6 4 4 4 4 5 5 5 4 15.0 20.0 10 0 25.0 30 0.0 2 1 1 DNA test The higher the quality of the study, the more ... It is not possible to predict the growth rate ... It is not possible to determine which of the a... Vase The medication increases recovery by 100% More than $102 Less than today with the money in this account False 500 10 1.0 59 out of 1000 25.0 20 Crosicol INR 1500,001 – INR 30,00,000 7000000.0 70 4 2 3 7000000.0 2 2 1 2 2 2 4 2 2 2 1 4 25.0 20 1 500 10 1.0 3 1 1 0 1 1 0 1 1 1 0 0 0 1 1 0 1 0 0 1 1 0
R_4zdzjECYeO2A5Ox 255 R_4zdzjECYeO2A5Ox Yes, I would like to participate in the study ... Female 19 Secondary School (11th to 12th Std.) Student 4 2 6 (strongly agree) 4 5 5 5 6 (strongly agree) 3 4 40.0 50.0 0 10 0.0 0 0.0 2 1 1 Fingerprint,DNA test The lower the quality of the study, the more l... The growth rate over five years will be exactl... 70 in 100 people prior to the intervention to ... Vase None of the above is implied More than $102 Less than today with the money in this account Do not know 500 10 0.1 9 out of 59 25.0 20 They are equal < INR 500,000 800000.0 19 2 3 1 800000.0 2 1 1 2 2 1 2 2 2 2 4 3 25.0 20 3 500 10 0.1 1 1 0 0 1 1 1 0 1 1 1 0 0 1 1 1 1 0 0 0 0 1
R_8k6D0jzzHCC5X3Z 369 R_8k6D0jzzHCC5X3Z Yes, I would like to participate in the study ... Male 25 Undergraduate Program Salaried/Employee/Consultant in a sector other... 7 (willing to take risk) 8 6 (strongly agree) 5 5 4 6 (strongly agree) 6 (strongly agree) 6 (strongly agree) 5 5.0 5.0 5 5 30.0 20 30.0 2 1 1 DNA test The higher the quality of the study, the more ... It is not possible to predict the growth rate ... 5 in 100 people prior to the intervention to 6... Vase The medication increases recovery by 100% More than $102 Less than today with the money in this account False 500 500 50.0 59 out of 1000 25.0 10 Hertinol > INR 75,00,000 8000000.0 25 2 2 5 8000000.0 2 2 1 2 2 2 4 2 2 2 1 1 25.0 10 2 500 500 50.0 3 1 1 0 1 1 0 1 1 0 0 0 0 1 0 0 1 0 0 1 0 0
R_8taN3wUPbeZMxzM 2 R_8taN3wUPbeZMxzM Yes, I would like to participate in the study ... Male 20 Undergraduate Program Student 3 0 (no investment experience) 6 (strongly agree) 6 (strongly agree) 4 1 (strongly disagree) 1 (strongly disagree) 2 1 (strongly disagree) 6 (strongly agree) 30.0 40.0 10 10 0.0 5 5.0 1 1 1 Fingerprint The higher the quality of the study, the more ... It is not possible to predict the growth rate ... 70 in 100 people prior to the intervention to ... Vase The medication increases recovery by 50% More than $102 More than today with the money in this account Do not know 1000 10 50.0 9 out of 59 50.0 20 They are equal < INR 500,000 10000.0 20 2 2 1 10000.0 2 1 2 2 2 2 4 1 1 1 2 3 50.0 20 3 1000 10 50.0 1 1 0 1 1 1 0 1 0 1 0 0 0 0 1 1 0 1 1 0 0 1

350 rows × 89 columns

In [28]:
d11 = d11Facet.copy().reset_index(drop = True)
In [29]:
d12 = scoring2(d11)
d12
Out[29]:
ResponseId age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_%
0 R_1YkMM2lMB9aEuVL 41 3 2 1 25000.0 4 0 2 1 1 0 80.0 0.0 40.0 33.333333 50.0 0.0 8 44.444444
1 R_40N8bUn3C0jwGzf 18 2 2 1 80000.0 1 0 2 1 0 1 20.0 0.0 40.0 33.333333 0.0 100.0 5 27.777778
2 R_40VyTmJ6i96wUP4 34 2 1 1 500000.0 4 1 3 1 0 0 80.0 50.0 60.0 33.333333 0.0 0.0 9 50.000000
3 R_40ZEg7vX3Y7mMQl 31 2 2 1 500000.0 1 0 2 2 2 0 20.0 0.0 40.0 66.666667 100.0 0.0 7 38.888889
4 R_40cbsHWzTyKyFxv 38 3 2 1 300000.0 4 0 2 2 0 0 80.0 0.0 40.0 66.666667 0.0 0.0 8 44.444444
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
345 R_4yzFrFYMa7w8nQZ 18 2 3 1 90000.0 4 0 3 2 0 0 80.0 0.0 60.0 66.666667 0.0 0.0 9 50.000000
346 R_4zPiSk23ayGvFD4 70 4 2 3 7000000.0 4 1 3 2 2 0 80.0 50.0 60.0 66.666667 100.0 0.0 12 66.666667
347 R_4zdzjECYeO2A5Ox 19 2 3 1 800000.0 3 1 1 3 2 1 60.0 50.0 20.0 100.000000 100.0 100.0 11 61.111111
348 R_8k6D0jzzHCC5X3Z 25 2 2 5 8000000.0 4 1 2 1 1 0 80.0 50.0 40.0 33.333333 50.0 0.0 9 50.000000
349 R_8taN3wUPbeZMxzM 20 2 2 1 10000.0 4 1 2 2 1 1 80.0 50.0 40.0 66.666667 50.0 100.0 11 61.111111

350 rows × 20 columns

In [30]:
df2 = pd.concat([d01, d12], axis = 0).reset_index(drop = True)
In [31]:
df2
Out[31]:
ResponseId age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_%
0 00ujdxbfoya0donu8r00ujcjdkojc99x 30.5 2 2 1 1500000.0 1 0 2 1 1 0 20.0 0.0 40.0 33.333333 50.0 0.0 5 27.777778
1 01ro2iftnum5v4cn01r03c75aa24emhq 21.5 2 2 2 500000.0 1 1 1 0 0 0 20.0 50.0 20.0 0.000000 0.0 0.0 3 16.666667
2 022xoawhrghfhv4a1g022xmz7hb0m41f 50.5 3 2 1 15000000.0 2 1 2 1 1 0 40.0 50.0 40.0 33.333333 50.0 0.0 7 38.888889
3 02pxtdbyibecqqfvwlw02pxwfbane9zd 50.5 3 1 2 3000000.0 1 1 3 1 0 0 20.0 50.0 60.0 33.333333 0.0 0.0 6 33.333333
4 037aefjdt26mnjd102nz0pk037aedfhc 50.5 3 1 2 7000000.0 4 1 1 1 0 1 80.0 50.0 20.0 33.333333 0.0 100.0 8 44.444444
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
834 R_4yzFrFYMa7w8nQZ 18.0 2 3 1 90000.0 4 0 3 2 0 0 80.0 0.0 60.0 66.666667 0.0 0.0 9 50.000000
835 R_4zPiSk23ayGvFD4 70.0 4 2 3 7000000.0 4 1 3 2 2 0 80.0 50.0 60.0 66.666667 100.0 0.0 12 66.666667
836 R_4zdzjECYeO2A5Ox 19.0 2 3 1 800000.0 3 1 1 3 2 1 60.0 50.0 20.0 100.000000 100.0 100.0 11 61.111111
837 R_8k6D0jzzHCC5X3Z 25.0 2 2 5 8000000.0 4 1 2 1 1 0 80.0 50.0 40.0 33.333333 50.0 0.0 9 50.000000
838 R_8taN3wUPbeZMxzM 20.0 2 2 1 10000.0 4 1 2 2 1 1 80.0 50.0 40.0 66.666667 50.0 100.0 11 61.111111

839 rows × 20 columns

In [ ]:
 

GRAPHS¶

In [32]:
# The following graph represents frequency of each data point on "TotalScore_18" or the total score out of 13 questions across the sample.

df2['TotalScore_18'].plot(kind = 'hist', xticks = np.arange(0, 20, step=1), xlabel = 'TotalScore_18', title = 'Frequency of Scores' )
Out[32]:
<Axes: title={'center': 'Frequency of Scores'}, xlabel='TotalScore_18', ylabel='Frequency'>
No description has been provided for this image
In [33]:
# Absolute mean scores for each facet

(df2[['Certainty_5', 'Uncertainty_2', 'RiskComprehension_5','GraphLiteracy_3','Numeracy_2','Bayesianreasoning_1','TotalScore_18']].mean(axis = 0)).plot(kind = 'bar', title = 'Absolute mean of scores for above data set')
Out[33]:
<Axes: title={'center': 'Absolute mean of scores for above data set'}>
No description has been provided for this image
In [34]:
# Normalised mean scores for each facet

df2[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean(axis = 0).plot(kind = 'bar', title = '% mean of scores for above data set')
Out[34]:
<Axes: title={'center': '% mean of scores for above data set'}>
No description has been provided for this image
In [ ]:
 
In [ ]:
 

ISCED¶

In [35]:
# Data Frame 5 or df5 is an aggregate data on facet total scores and over all total score, along with  education data vs each response. 
# This data set is now sorted by education.

df5 = df2.sort_values(by = 'isced')
df5 = df5.reset_index(drop = True)

df5
Out[35]:
ResponseId age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_%
0 R_4dyDxqwOf6c5bUt 46.0 3 1 2 4000000.0 4 1 1 2 2 1 80.0 50.0 20.0 66.666667 100.0 100.0 11 61.111111
1 R_4VgFPHXva4gJjhf 21.0 2 1 2 900000.0 4 0 1 2 0 0 80.0 0.0 20.0 66.666667 0.0 0.0 7 38.888889
2 R_4Vwgb0WSlLsIYlL 44.0 3 1 1 10000000.0 4 1 3 1 1 0 80.0 50.0 60.0 33.333333 50.0 0.0 10 55.555556
3 p15xolgvzls8wctop15q6ithwa3t8msq 40.5 3 1 3 50000000.0 0 0 2 3 1 0 0.0 0.0 40.0 100.000000 50.0 0.0 6 33.333333
4 oza83gum5po6u3jk4oza83ad7gtuqvzd 30.5 2 1 4 150000.0 1 0 1 0 1 1 20.0 0.0 20.0 0.000000 50.0 100.0 4 22.222222
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
834 R_42AoOsYAIUhnViQ 35.0 2 3 3 5000.0 4 1 2 2 0 0 80.0 50.0 40.0 66.666667 0.0 0.0 9 50.000000
835 870h2qs77chp1cd5q870n4fnkjbrxezk 21.5 2 3 2 150000.0 2 0 0 0 0 1 40.0 0.0 0.0 0.000000 0.0 100.0 3 16.666667
836 8eynniv5jpifaxbqslk8eynnw7he4d4q 40.5 3 3 1 200000.0 1 2 1 0 0 0 20.0 100.0 20.0 0.000000 0.0 0.0 4 22.222222
837 sw8rv2orxir5asm6jn8sw8rv24olibmz 21.5 2 3 1 30000.0 1 0 3 0 1 0 20.0 0.0 60.0 0.000000 50.0 0.0 5 27.777778
838 R_4dE2pi4mUJFadlk 18.0 2 3 1 50000.0 4 2 3 3 0 0 80.0 100.0 60.0 100.000000 0.0 0.0 12 66.666667

839 rows × 20 columns

In [36]:
# Mean of each facet as a % for each category of isced

df5.groupby('isced')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%']].mean()
Out[36]:
Certainty_% Uncertainty_% RiskComprehension_% Numeracy_% GraphLiteracy_% Bayesianreasoning_%
isced
1 44.987893 22.760291 44.116223 53.874092 44.552058 19.612591
2 52.747253 22.390110 44.285714 54.532967 45.879121 25.549451
3 62.903226 33.870968 41.612903 48.387097 44.086022 22.580645
In [37]:
# Count of responses for each category isced

df5.groupby('isced')[['ResponseId']].count()
Out[37]:
ResponseId
isced
1 413
2 364
3 62
In [38]:
# Absolute Total Facet scores mean line plot from low to high ISCED

df5.groupby('isced')[['TotalScore_18']].mean().plot( kind = 'line', title = 'Absolute Total Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[38]:
<matplotlib.legend.Legend at 0x293e7e8b7d0>
No description has been provided for this image
In [39]:
# Absolute Facet scores mean line plot from low to high edu

df5.groupby('isced')[['Certainty_5', "Uncertainty_2", 'RiskComprehension_5','Numeracy_2','GraphLiteracy_3','Bayesianreasoning_1']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[39]:
<matplotlib.legend.Legend at 0x293e7e58290>
No description has been provided for this image
In [40]:
# Normalised Facet scores mean line plot from low to high edu


df5.groupby('isced')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().plot( kind = 'line', title = 'Normalised Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[40]:
<matplotlib.legend.Legend at 0x293e61a7650>
No description has been provided for this image
In [41]:
# Normalised mean scores for each facet stacked

df5.groupby('isced')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%']].mean().plot( kind = 'bar', title = 'Normalised mean scores for each facet stacked', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[41]:
<matplotlib.legend.Legend at 0x293e5f4d390>
No description has been provided for this image
In [42]:
# Normalised mean scores for each facet for each edu response category

df5.groupby('isced')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().T.plot(kind = 'bar', title = 'Normalised mean scores for each facet for each edu response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[42]:
<matplotlib.legend.Legend at 0x293e5e43110>
No description has been provided for this image
In [43]:
# Trend line for Absolute Total Facet Score vs edu (isced) reponses

sns.regplot (data = df5, x = 'isced', y = 'TotalScore_18')
Out[43]:
<Axes: xlabel='isced', ylabel='TotalScore_18'>
No description has been provided for this image
In [44]:
# Trend line for Absolute Independent Facet Score vs edu (isced) reponses

fig, ax6 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df5, x = 'isced', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax6, label='Certainty_5')
sns.regplot (data = df5, x = 'isced', y = 'Uncertainty_2', fit_reg=True, ci=None, ax=ax6, label='Uncertainty_2')
sns.regplot (data = df5, x = 'isced', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax6, label='RiskComprehension_5')
sns.regplot (data = df5, x = 'isced', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax6, label='Numeracy_2')
sns.regplot (data = df5, x = 'isced', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax6, label='GraphLiteracy_3')
sns.regplot (data = df5, x = 'isced', y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax6, label='Bayesianreasoning_1' )

ax6.set(ylabel='Scores', xlabel='isced')
ax6.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [45]:
# Violine Plot for TotalScore_18 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'TotalScore_18')
Out[45]:
<Axes: xlabel='isced', ylabel='TotalScore_18'>
No description has been provided for this image
In [46]:
# Violine Plot for Certainty_5 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Certainty_5')
Out[46]:
<Axes: xlabel='isced', ylabel='Certainty_5'>
No description has been provided for this image
In [47]:
# Violine Plot for Uncertainty_5 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Uncertainty_2')
Out[47]:
<Axes: xlabel='isced', ylabel='Uncertainty_2'>
No description has been provided for this image
In [48]:
# Violine Plot for RiskComprehension_5 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'RiskComprehension_5')
Out[48]:
<Axes: xlabel='isced', ylabel='RiskComprehension_5'>
No description has been provided for this image
In [49]:
# Violine Plot for GraphLiteracy_3 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'GraphLiteracy_3')
Out[49]:
<Axes: xlabel='isced', ylabel='GraphLiteracy_3'>
No description has been provided for this image
In [50]:
# Violine Plot for Numeracy_2 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Numeracy_2')
Out[50]:
<Axes: xlabel='isced', ylabel='Numeracy_2'>
No description has been provided for this image
In [51]:
# Violine Plot for TotalScore_18 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Bayesianreasoning_1')
Out[51]:
<Axes: xlabel='isced', ylabel='Bayesianreasoning_1'>
No description has been provided for this image
In [ ]:
 
In [ ]:
 
In [ ]:
 
In [ ]:
 
In [ ]:
 

INCOME¶

INCOME VS SCORES¶

In [52]:
# DF3 = Sorted by income

df3 = df2.sort_values(by = 'income')
df3 = df3.reset_index(drop = True)
df3.drop(df3[df3['income'] == 7].index, inplace = True)

df3
Out[52]:
ResponseId age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_%
0 00ujdxbfoya0donu8r00ujcjdkojc99x 30.5 2 2 1 1500000.0 1 0 2 1 1 0 20.0 0.0 40.0 33.333333 50.0 0.0 5 27.777778
1 R_4C2deHiFGOXeYRv 23.0 2 2 1 1000000.0 4 1 2 2 0 0 80.0 50.0 40.0 66.666667 0.0 0.0 9 50.000000
2 R_4C2WItrJsoTF1cE 19.0 2 2 1 500000.0 4 1 3 1 2 1 80.0 50.0 60.0 33.333333 100.0 100.0 12 66.666667
3 R_4BXfZlfrPGt8mgt 21.0 2 3 1 10000.0 4 1 2 2 1 0 80.0 50.0 40.0 66.666667 50.0 0.0 10 55.555556
4 R_4Ao5lbx6bkiG9GN 38.0 3 1 1 100000.0 4 0 3 3 0 0 80.0 0.0 60.0 100.000000 0.0 0.0 10 55.555556
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
834 5pjohovg20dh7j4x5pjohucrvulgs19t 50.5 3 1 5 7500000.0 2 1 3 0 2 0 40.0 50.0 60.0 0.000000 100.0 0.0 8 44.444444
835 R_4rGgRh3Y7Grn2f5 20.0 2 2 5 500000.0 4 1 1 0 0 0 80.0 50.0 20.0 0.000000 0.0 0.0 6 33.333333
836 R_4GeNwNOEgwD5rwL 19.0 2 3 5 400000.0 4 1 2 1 1 0 80.0 50.0 40.0 33.333333 50.0 0.0 9 50.000000
837 R_48SQkLXmsHzNHIf 25.0 2 2 5 100.0 4 0 3 1 0 1 80.0 0.0 60.0 33.333333 0.0 100.0 9 50.000000
838 gs539g4qtok8tm7rmcjegs539g7ozj75 50.5 3 1 5 10000000.0 2 0 2 1 1 1 40.0 0.0 40.0 33.333333 50.0 100.0 7 38.888889

839 rows × 20 columns

In [53]:
# Normalised mean of each facet as a numerical (sorted by income)

df3.groupby('income')[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean()
Out[53]:
Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_%
income
1 58.053691 42.885906 48.210291 53.020134 25.167785 46.290082
2 44.802432 44.316109 44.984802 55.927052 21.276596 42.046606
3 45.818182 47.818182 44.545455 53.636364 20.000000 42.979798
4 43.076923 42.769231 37.948718 50.769231 21.538462 39.572650
5 48.648649 41.081081 35.135135 45.945946 18.918919 39.189189
In [54]:
# Count of responses for each category (sorted by income)

df3.groupby('income')[['ResponseId']].count()
Out[54]:
ResponseId
income
1 298
2 329
3 110
4 65
5 37
In [55]:
# Absolute mean of Total Facet score line plot (sorted by income)

df3.groupby('income')[['TotalScore_18']].mean().plot( kind = 'line', title = 'Absolute mean of Total Facet score line plot (sorted by income)').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[55]:
<matplotlib.legend.Legend at 0x293e7aead90>
No description has been provided for this image
In [56]:
# Absolute mean of each Facet score line plot (sorted by income)

df3.groupby('income')[['Certainty_5', "Uncertainty_2", 'RiskComprehension_5','GraphLiteracy_3','Numeracy_2','Bayesianreasoning_1']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[56]:
<matplotlib.legend.Legend at 0x293e7b15b10>
No description has been provided for this image
In [57]:
# Normalised mean of each Facet score line plot (sorted by income)


df3.groupby('income')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%', 'TotalScore_%']].mean().plot( kind = 'line', title = '% mean of each Facet score line plot (sorted by income)').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[57]:
<matplotlib.legend.Legend at 0x293e79f3810>
No description has been provided for this image
In [58]:
# Normalised mean of each Facet score stacked plot (sorted by income)

df3.groupby('income')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%']].mean().plot( kind = 'bar', title = '% mean of each Facet score stacked plot (sorted by income)', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[58]:
<matplotlib.legend.Legend at 0x293e7a42d50>
No description has been provided for this image
In [59]:
# Normalised mean of each Facet score hist plot (sorted by income)

df3.groupby('income')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean().T.plot(kind = 'bar', title = '% mean scores for each facet for each income response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[59]:
<matplotlib.legend.Legend at 0x293e731bb90>
No description has been provided for this image
In [60]:
# Trend line for Absolute Total Facet Score vs income

sns.regplot (data = df3, x = 'income', y = 'TotalScore_18')
Out[60]:
<Axes: xlabel='income', ylabel='TotalScore_18'>
No description has been provided for this image
In [61]:
# Trend line for Absolute Independent Facet Score vs income reponses

fig, ax = plt.subplots(figsize=(6, 6))

sns.regplot (data = df3, x = 'income', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax, label='Certainty_5')
sns.regplot (data = df3, x = 'income', y = 'Uncertainty_2', fit_reg=True, ci=None, ax=ax, label='Uncertainty_2')
sns.regplot (data = df3, x = 'income', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax, label='RiskComprehension_5')
sns.regplot (data = df3, x = 'income', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax, label='Numeracy_2')
sns.regplot (data = df3, x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax, label='GraphLiteracy_3')
sns.regplot (data = df3, x = 'income', y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax, label='Bayesianreasoning_1' )

ax.set(ylabel='Scores', xlabel='income')
ax.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [62]:
# Violin Plot for TotalScore_18 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'TotalScore_18')
Out[62]:
<Axes: xlabel='income', ylabel='TotalScore_18'>
No description has been provided for this image
In [63]:
# Violin Plot for Certainty_5 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Certainty_5')
Out[63]:
<Axes: xlabel='income', ylabel='Certainty_5'>
No description has been provided for this image
In [64]:
# Violin Plot for Certainty_5 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Uncertainty_2')
Out[64]:
<Axes: xlabel='income', ylabel='Uncertainty_2'>
No description has been provided for this image
In [65]:
# Violine Plot for RiskComprehension_5 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'RiskComprehension_5')
Out[65]:
<Axes: xlabel='income', ylabel='RiskComprehension_5'>
No description has been provided for this image
In [66]:
# Violine Plot for GraphLiteracy_3 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'GraphLiteracy_3')
Out[66]:
<Axes: xlabel='income', ylabel='GraphLiteracy_3'>
No description has been provided for this image
In [67]:
# Violine Plot for Numeracy_2 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Numeracy_2')
Out[67]:
<Axes: xlabel='income', ylabel='Numeracy_2'>
No description has been provided for this image
In [68]:
# Violine Plot for TotalScore_18 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Bayesianreasoning_1')
Out[68]:
<Axes: xlabel='income', ylabel='Bayesianreasoning_1'>
No description has been provided for this image
In [ ]:
 
In [ ]:
 

INCOME vs SCORES w/ ISCED classification¶

In [69]:
# Descriptive stats for the data set, isced = 1
# NA values of income are removed

df7 = df5
df7.drop(df7[df7['income'] == 7].index, inplace = True)

df7.loc[df7['isced']==1].describe()
Out[69]:
age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_%
count 413.000000 413.000000 413.0 413.000000 4.130000e+02 413.000000 413.000000 413.000000 413.000000 413.000000 413.000000 413.000000 413.000000 413.000000 413.000000 413.000000 413.000000 413.000000 413.000000
mean 35.285714 2.443099 1.0 2.314770 1.020335e+07 2.249395 0.455206 2.205811 1.336562 1.077482 0.196126 44.987893 22.760291 44.116223 44.552058 53.874092 19.612591 7.520581 41.781006
std 9.828602 0.582747 0.0 1.091761 3.794182e+07 1.293479 0.616168 1.069781 0.963179 0.771961 0.397546 25.869582 30.808415 21.395627 32.105960 38.598049 39.754648 2.523229 14.017942
min 18.000000 2.000000 1.0 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2.000000 11.111111
25% 30.500000 2.000000 1.0 2.000000 3.000000e+05 1.000000 0.000000 1.000000 1.000000 0.000000 0.000000 20.000000 0.000000 20.000000 33.333333 0.000000 0.000000 6.000000 33.333333
50% 30.500000 2.000000 1.0 2.000000 1.200000e+06 2.000000 0.000000 2.000000 1.000000 1.000000 0.000000 40.000000 0.000000 40.000000 33.333333 50.000000 0.000000 7.000000 38.888889
75% 40.500000 3.000000 1.0 3.000000 5.000000e+06 3.000000 1.000000 3.000000 2.000000 2.000000 0.000000 60.000000 50.000000 60.000000 66.666667 100.000000 0.000000 9.000000 50.000000
max 70.500000 4.000000 1.0 5.000000 5.000000e+08 5.000000 2.000000 5.000000 3.000000 2.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 15.000000 83.333333
In [70]:
df7.loc[df7['isced']==2].describe()
Out[70]:
age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_%
count 364.000000 364.000000 364.0 364.000000 3.640000e+02 364.000000 364.000000 364.000000 364.000000 364.000000 364.000000 364.000000 364.000000 364.000000 364.000000 364.000000 364.000000 364.000000 364.000000
mean 30.877747 2.271978 2.0 1.851648 9.861171e+06 2.637363 0.447802 2.214286 1.376374 1.090659 0.255495 52.747253 22.390110 44.285714 45.879121 54.532967 25.549451 8.021978 44.566545
std 10.056769 0.514458 0.0 1.017734 5.225548e+07 1.323794 0.612110 1.019487 0.995080 0.823231 0.436739 26.475871 30.605488 20.389748 33.169331 41.161531 43.673914 2.646180 14.701002
min 18.000000 2.000000 2.0 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 5.555556
25% 22.000000 2.000000 2.0 1.000000 2.000000e+05 1.000000 0.000000 2.000000 1.000000 0.000000 0.000000 20.000000 0.000000 40.000000 33.333333 0.000000 0.000000 6.000000 33.333333
50% 30.500000 2.000000 2.0 2.000000 6.000000e+05 3.000000 0.000000 2.000000 1.000000 1.000000 0.000000 60.000000 0.000000 40.000000 33.333333 50.000000 0.000000 8.000000 44.444444
75% 34.000000 2.000000 2.0 2.000000 3.000000e+06 4.000000 1.000000 3.000000 2.000000 2.000000 1.000000 80.000000 50.000000 60.000000 66.666667 100.000000 100.000000 10.000000 55.555556
max 70.000000 4.000000 2.0 5.000000 6.000000e+08 5.000000 2.000000 5.000000 3.000000 2.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 15.000000 83.333333
In [71]:
df7.loc[df7['isced']==3].describe()
Out[71]:
age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_%
count 62.000000 62.000000 62.0 62.000000 6.200000e+01 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000
mean 26.951613 2.225806 3.0 1.629032 3.719399e+06 3.145161 0.677419 2.080645 1.322581 0.967742 0.225806 62.903226 33.870968 41.612903 44.086022 48.387097 22.580645 8.419355 46.774194
std 12.274093 0.584481 0.0 1.119629 1.336024e+07 1.157255 0.672022 0.892563 0.971296 0.829136 0.421526 23.145094 33.601075 17.851257 32.376538 41.456813 42.152552 2.583354 14.351966
min 16.000000 0.000000 3.0 1.000000 0.000000e+00 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 20.000000 0.000000 0.000000 0.000000 0.000000 0.000000 3.000000 16.666667
25% 19.000000 2.000000 3.0 1.000000 5.000000e+04 2.000000 0.000000 2.000000 1.000000 0.000000 0.000000 40.000000 0.000000 40.000000 33.333333 0.000000 0.000000 7.000000 38.888889
50% 21.250000 2.000000 3.0 1.000000 2.000000e+05 4.000000 1.000000 2.000000 1.000000 1.000000 0.000000 80.000000 50.000000 40.000000 33.333333 50.000000 0.000000 9.000000 50.000000
75% 30.500000 2.000000 3.0 2.000000 1.500000e+06 4.000000 1.000000 3.000000 2.000000 2.000000 0.000000 80.000000 50.000000 60.000000 66.666667 100.000000 0.000000 10.000000 55.555556
max 70.500000 4.000000 3.0 5.000000 1.000000e+08 5.000000 2.000000 4.000000 3.000000 2.000000 1.000000 100.000000 100.000000 80.000000 100.000000 100.000000 100.000000 12.000000 66.666667
In [72]:
# Trend line for Absolute Tota Facet Score vs income reponses sorted by isced and ORDERED by income WITH scatter

fig, ax7 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = df7.loc[df7['isced']==1]['TotalScore_18'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 1')
sns.regplot (data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = df7.loc[df7['isced']==2]['TotalScore_18'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 2')
sns.regplot (data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = df7.loc[df7['isced']==3]['TotalScore_18'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 3')

ax7.set(ylabel='Total Scores_19', xlabel='INCOME')
ax7.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [73]:
# Trend line for Absolute Tota Facet Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax8 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 3')

ax8.set(ylabel='Total Scores_19', xlabel='INCOME')
ax8.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [74]:
# Trend line for Absolute Certainty Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax9 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 3')

ax9.set(ylabel='Certainty_5', xlabel='INCOME')
ax9.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [75]:
# Trend line for Absolute Risk Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax10 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 3')

ax10.set(ylabel='RiskComprehension_5', xlabel='INCOME')
ax10.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [76]:
# Trend line for Absolute Number Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Numeracy_2', xlabel='INCOME')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [77]:
# Trend line for Absolute Graph Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='GraphLiteracy_3', xlabel='INCOME')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [78]:
# Trend line for Absolute Bayesian Reasoning Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax12 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 3')

ax12.set(ylabel='Bayesianreasoning_1', xlabel='INCOME')
ax12.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [ ]:
 
In [ ]:
 

INCOME vs SCORES w/ ISCED and AGE based classification¶

In [79]:
df7
Out[79]:
ResponseId age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_%
0 R_4dyDxqwOf6c5bUt 46.0 3 1 2 4000000.0 4 1 1 2 2 1 80.0 50.0 20.0 66.666667 100.0 100.0 11 61.111111
1 R_4VgFPHXva4gJjhf 21.0 2 1 2 900000.0 4 0 1 2 0 0 80.0 0.0 20.0 66.666667 0.0 0.0 7 38.888889
2 R_4Vwgb0WSlLsIYlL 44.0 3 1 1 10000000.0 4 1 3 1 1 0 80.0 50.0 60.0 33.333333 50.0 0.0 10 55.555556
3 p15xolgvzls8wctop15q6ithwa3t8msq 40.5 3 1 3 50000000.0 0 0 2 3 1 0 0.0 0.0 40.0 100.000000 50.0 0.0 6 33.333333
4 oza83gum5po6u3jk4oza83ad7gtuqvzd 30.5 2 1 4 150000.0 1 0 1 0 1 1 20.0 0.0 20.0 0.000000 50.0 100.0 4 22.222222
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
834 R_42AoOsYAIUhnViQ 35.0 2 3 3 5000.0 4 1 2 2 0 0 80.0 50.0 40.0 66.666667 0.0 0.0 9 50.000000
835 870h2qs77chp1cd5q870n4fnkjbrxezk 21.5 2 3 2 150000.0 2 0 0 0 0 1 40.0 0.0 0.0 0.000000 0.0 100.0 3 16.666667
836 8eynniv5jpifaxbqslk8eynnw7he4d4q 40.5 3 3 1 200000.0 1 2 1 0 0 0 20.0 100.0 20.0 0.000000 0.0 0.0 4 22.222222
837 sw8rv2orxir5asm6jn8sw8rv24olibmz 21.5 2 3 1 30000.0 1 0 3 0 1 0 20.0 0.0 60.0 0.000000 50.0 0.0 5 27.777778
838 R_4dE2pi4mUJFadlk 18.0 2 3 1 50000.0 4 2 3 3 0 0 80.0 100.0 60.0 100.000000 0.0 0.0 12 66.666667

839 rows × 20 columns

In [80]:
# Since we already have a classification for Age groups in the form of age_rec, we will use that.
# We will also use median of age to see if it yields any relevant results, as instructed.

# AXES to be used = Age or age groups, ISCED, Income

# Age groups = [2,3,4]
# Age group 2 = 18 to 35 y/o
# Age group 3 = 36 to 59 y/o
# Age group 4 = 60 to 75 y/o (75 y/o, i.e, within the scope of the data we have, it can mean 60 and above also)
In [81]:
df7.loc[(df7['age_rec']==2)].describe()
Out[81]:
age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_%
count 573.000000 573.0 573.000000 573.000000 5.730000e+02 573.000000 573.000000 573.000000 573.000000 573.000000 573.000000 573.000000 573.000000 573.000000 573.000000 573.000000 573.000000 573.000000 573.000000
mean 26.943281 2.0 1.647469 1.959860 7.616613e+06 2.554974 0.476440 2.158813 1.371728 1.085515 0.235602 51.099476 23.821990 43.176265 45.724258 54.275742 23.560209 7.883072 43.794842
std 4.633303 0.0 0.626887 1.047872 3.540355e+07 1.350679 0.638141 1.016084 0.973263 0.805151 0.424745 27.013572 31.907039 20.321679 32.442106 40.257572 42.474533 2.596786 14.426588
min 18.000000 2.0 1.000000 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 5.555556
25% 22.000000 2.0 1.000000 1.000000 2.000000e+05 1.000000 0.000000 1.000000 1.000000 0.000000 0.000000 20.000000 0.000000 20.000000 33.333333 0.000000 0.000000 6.000000 33.333333
50% 30.000000 2.0 2.000000 2.000000 6.500000e+05 3.000000 0.000000 2.000000 1.000000 1.000000 0.000000 60.000000 0.000000 40.000000 33.333333 50.000000 0.000000 8.000000 44.444444
75% 30.500000 2.0 2.000000 2.000000 3.000000e+06 4.000000 1.000000 3.000000 2.000000 2.000000 0.000000 80.000000 50.000000 60.000000 66.666667 100.000000 0.000000 10.000000 55.555556
max 35.000000 2.0 3.000000 5.000000 5.000000e+08 5.000000 2.000000 5.000000 3.000000 2.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 15.000000 83.333333
In [82]:
df7.loc[(df7['age_rec']==3)].describe()
Out[82]:
age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_%
count 232.000000 232.0 232.000000 232.000000 2.320000e+02 232.000000 232.000000 232.000000 232.000000 232.000000 232.000000 232.000000 232.000000 232.000000 232.000000 232.00000 232.000000 232.000000 232.000000
mean 42.892241 3.0 1.426724 2.306034 1.445481e+07 2.314655 0.448276 2.271552 1.284483 1.047414 0.198276 46.293103 22.413793 45.431034 42.816092 52.37069 19.827586 7.564655 42.025862
std 4.753812 0.0 0.591258 1.138221 6.124404e+07 1.282635 0.578771 1.048380 0.987419 0.790853 0.399563 25.652698 28.938528 20.967600 32.913966 39.54263 39.956313 2.591219 14.395662
min 36.000000 3.0 1.000000 1.000000 3.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 2.000000 11.111111
25% 40.500000 3.0 1.000000 1.750000 3.000000e+05 1.000000 0.000000 2.000000 0.000000 0.000000 0.000000 20.000000 0.000000 40.000000 0.000000 0.00000 0.000000 6.000000 33.333333
50% 40.500000 3.0 1.000000 2.000000 1.750000e+06 2.000000 0.000000 2.000000 1.000000 1.000000 0.000000 40.000000 0.000000 40.000000 33.333333 50.00000 0.000000 8.000000 44.444444
75% 46.500000 3.0 2.000000 3.000000 7.500000e+06 4.000000 1.000000 3.000000 2.000000 2.000000 0.000000 80.000000 50.000000 60.000000 66.666667 100.00000 0.000000 9.000000 50.000000
max 54.000000 3.0 3.000000 5.000000 6.000000e+08 5.000000 2.000000 5.000000 3.000000 2.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.00000 100.000000 14.000000 77.777778
In [83]:
df7.loc[(df7['age_rec']==4)].describe()
Out[83]:
age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_%
count 33.000000 33.0 33.000000 33.000000 3.300000e+01 33.000000 33.000000 33.000000 33.000000 33.000000 33.000000 33.000000 33.000000 33.000000 33.000000 33.000000 33.000000 33.000000 33.000000
mean 62.969697 4.0 1.484848 2.181818 9.582270e+06 2.393939 0.454545 2.393939 1.545455 1.090909 0.212121 47.878788 22.727273 47.878788 51.515152 54.545455 21.212121 8.090909 44.949495
std 4.390934 0.0 0.618527 1.236288 1.406214e+07 0.966288 0.616994 1.248484 0.938446 0.765001 0.415149 19.325756 30.849709 24.969679 31.281550 38.250074 41.514875 2.602446 14.458036
min 57.000000 4.0 1.000000 1.000000 1.000000e+05 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 20.000000 0.000000 0.000000 0.000000 0.000000 0.000000 3.000000 16.666667
25% 60.500000 4.0 1.000000 1.000000 1.250000e+06 2.000000 0.000000 1.000000 1.000000 1.000000 0.000000 40.000000 0.000000 20.000000 33.333333 50.000000 0.000000 6.000000 33.333333
50% 60.500000 4.0 1.000000 2.000000 5.000000e+06 2.000000 0.000000 2.000000 2.000000 1.000000 0.000000 40.000000 0.000000 40.000000 66.666667 50.000000 0.000000 8.000000 44.444444
75% 65.000000 4.0 2.000000 2.000000 1.000000e+07 3.000000 1.000000 3.000000 2.000000 2.000000 0.000000 60.000000 50.000000 60.000000 66.666667 100.000000 0.000000 10.000000 55.555556
max 70.500000 4.0 3.000000 5.000000 5.250000e+07 4.000000 2.000000 5.000000 3.000000 2.000000 1.000000 80.000000 100.000000 100.000000 100.000000 100.000000 100.000000 13.000000 72.222222
In [ ]:
 
In [84]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_14', xlabel='INCOME for age 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_14', xlabel='INCOME for age 36 to 55')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_14', xlabel='INCOME for age 56 and above')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
In [85]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ df7['age_rec']==2  ]["income"].unique(), y = df7.loc[ df7['age_rec']==2  ].groupby(['income']).mean(numeric_only=True)['TotalScore_18'], yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Total Scores_14', xlabel='INCOME for age 18 to 35',  yticks = np.arange(5, 16 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_14', xlabel='INCOME for age 36 to 55',  yticks = np.arange(5, 16 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df7.loc[ df7['age_rec']==3  ]["income"].unique(), y = df7.loc[ df7['age_rec']==3  ].groupby(['income']).mean(numeric_only=True)['TotalScore_18'], yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_14', xlabel='INCOME for age 56 and above',  yticks = np.arange(5, 16 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income']).mean(numeric_only=True)['TotalScore_18'], yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [86]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Certainty_5', xlabel='INCOME for age 18 to 35',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df7.loc[ df7['age_rec']==2  ]["income"].unique(), y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['Certainty_5'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Certainty_5', xlabel='INCOME for age 36 to 55',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df7.loc[ df7['age_rec']==3  ]["income"].unique(), y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['Certainty_5'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Certainty_5', xlabel='INCOME for age 56 and above',  yticks = np.arange(0, 6 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['Certainty_5'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [87]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='RiskComprehension_5', xlabel='INCOME for age 18 to 35',  yticks = np.arange(0, 3 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df7.loc[ df7['age_rec']==2  ]["income"].unique(), y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['RiskComprehension_5'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='RiskComprehension_5', xlabel='INCOME for age 36 to 55',  yticks = np.arange(0, 3 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df7.loc[ df7['age_rec']==3  ]["income"].unique(), y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['RiskComprehension_5'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='RiskComprehension_5', xlabel='INCOME for age 56 and above',  yticks = np.arange(0, 3 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['RiskComprehension_5'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [88]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Numeracy_2', xlabel='INCOME for age 18 to 35',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df7.loc[ df7['age_rec']==2  ]["income"].unique(), y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['Numeracy_2'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Numeracy_2', xlabel='INCOME for age 36 to 55',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df7.loc[ df7['age_rec']==3  ]["income"].unique(), y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['Numeracy_2'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Numeracy_2', xlabel='INCOME for age 56 and above',  yticks = np.arange(0, 6 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['Numeracy_2'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [89]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='GraphLiteracy_3', xlabel='INCOME for age 18 to 35',  yticks = np.arange(0, 4 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df7.loc[ df7['age_rec']==2  ]["income"].unique(), y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='GraphLiteracy_3', xlabel='INCOME for age 36 to 55',  yticks = np.arange(0, 4 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df7.loc[ df7['age_rec']==3  ]["income"].unique(), y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='GraphLiteracy_3', xlabel='INCOME for age 56 and above',  yticks = np.arange(0, 4 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [90]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for age 18 to 35',  yticks = np.arange(0, 5 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df7.loc[ df7['age_rec']==2  ]["income"].unique(), y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for age 36 to ',  yticks = np.arange(0, 5 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df7.loc[ df7['age_rec']==3  ]["income"].unique(), y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for age 56 and above',  yticks = np.arange(0, 5 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [91]:
# Age mean method
# Age_mean
In [92]:
age_m = df2["age"].median()
age_m
Out[92]:
30.5
In [ ]:
 
In [93]:
df7["income"].value_counts()
Out[93]:
income
2    329
1    298
3    110
4     65
5     37
Name: count, dtype: int64
In [94]:
df7.loc[(df7['age'] < age_m)].describe()
Out[94]:
age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_%
count 298.000000 298.000000 298.000000 298.000000 2.980000e+02 298.000000 298.000000 298.000000 298.000000 298.000000 298.000000 298.000000 298.000000 298.000000 298.000000 298.000000 298.000000 298.000000 298.000000
mean 23.206376 1.993289 1.869128 1.788591 7.382464e+06 3.036913 0.553691 2.171141 1.459732 1.151007 0.271812 60.738255 27.684564 43.422819 48.657718 57.550336 27.181208 8.644295 48.023863
std 3.435092 0.115857 0.635366 1.050383 3.704148e+07 1.248021 0.665759 0.939718 0.984761 0.804459 0.445642 24.960412 33.287967 18.794362 32.825354 40.222937 44.564190 2.504068 13.911487
min 16.000000 0.000000 1.000000 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2.000000 11.111111
25% 21.000000 2.000000 1.000000 1.000000 1.000000e+05 2.000000 0.000000 2.000000 1.000000 0.000000 0.000000 40.000000 0.000000 40.000000 33.333333 0.000000 0.000000 7.000000 38.888889
50% 22.000000 2.000000 2.000000 1.000000 5.000000e+05 4.000000 0.000000 2.000000 1.000000 1.000000 0.000000 80.000000 0.000000 40.000000 33.333333 50.000000 0.000000 9.000000 50.000000
75% 26.000000 2.000000 2.000000 2.000000 2.000000e+06 4.000000 1.000000 3.000000 2.000000 2.000000 1.000000 80.000000 50.000000 60.000000 66.666667 100.000000 100.000000 10.000000 55.555556
max 30.000000 2.000000 3.000000 5.000000 5.000000e+08 4.000000 2.000000 5.000000 3.000000 2.000000 1.000000 80.000000 100.000000 100.000000 100.000000 100.000000 100.000000 15.000000 83.333333
In [95]:
df7.loc[(df7['age'] >= age_m)].describe()
Out[95]:
age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_%
count 541.000000 541.000000 541.000000 541.000000 5.410000e+02 541.000000 541.000000 541.000000 541.000000 541.000000 541.000000 541.000000 541.000000 541.000000 541.000000 541.000000 541.000000 541.000000 541.000000
mean 38.018484 2.550832 1.423290 2.214418 1.078387e+07 2.179298 0.421442 2.216266 1.293900 1.033272 0.197782 43.585952 21.072089 44.325323 43.130006 51.663586 19.778189 7.341959 40.788663
std 9.235180 0.608356 0.561193 1.084510 4.690035e+07 1.263159 0.589902 1.084996 0.968265 0.792793 0.398695 25.263180 29.495078 21.699921 32.275495 39.639645 39.869541 2.530458 14.058100
min 30.500000 2.000000 1.000000 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 5.555556
25% 30.500000 2.000000 1.000000 1.000000 3.000000e+05 1.000000 0.000000 1.000000 1.000000 0.000000 0.000000 20.000000 0.000000 20.000000 33.333333 0.000000 0.000000 6.000000 33.333333
50% 35.000000 2.000000 1.000000 2.000000 1.200000e+06 2.000000 0.000000 2.000000 1.000000 1.000000 0.000000 40.000000 0.000000 40.000000 33.333333 50.000000 0.000000 7.000000 38.888889
75% 40.500000 3.000000 2.000000 3.000000 5.000000e+06 3.000000 1.000000 3.000000 2.000000 2.000000 0.000000 60.000000 50.000000 60.000000 66.666667 100.000000 0.000000 9.000000 50.000000
max 70.500000 4.000000 3.000000 5.000000 6.000000e+08 5.000000 2.000000 5.000000 3.000000 2.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 14.000000 77.777778
In [96]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_19', xlabel='INCOME for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age'] >= age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_19', xlabel='INCOME for >=median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [97]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['TotalScore_18'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Total Scores_19', xlabel='INCOME for < median age',  yticks = np.arange(5, 16 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] >= age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['TotalScore_18'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Total Scores_19', xlabel='INCOME for >=median age',  yticks = np.arange(5, 16 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [98]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['Certainty_5'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Certainty_5', xlabel='INCOME for < median age',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] >= age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['Certainty_5'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Certainty_5', xlabel='INCOME for >=median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [99]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['RiskComprehension_5'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='RiskComprehension_5', xlabel='INCOME for < median age',  yticks = np.arange(0, 3 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] >= age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['RiskComprehension_5'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='RiskComprehension_5', xlabel='INCOME for >=median age',  yticks = np.arange(0, 3 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [100]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['Numeracy_2'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Numeracy_2', xlabel='INCOME for < median age',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] >= age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['Numeracy_2'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Numeracy_2', xlabel='INCOME for >=median age',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [101]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='GraphLiteracy_3', xlabel='INCOME for < median age',  yticks = np.arange(0, 4 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] >= age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='GraphLiteracy_3', xlabel='INCOME for >=median age',  yticks = np.arange(0, 4 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [102]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for < median age',  yticks = np.arange(0, 5 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] >= age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for >=median age',  yticks = np.arange(0, 5 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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WEALTH¶

In [103]:
# Data Frame 2 or df2 is an aggregate data on facet total scores and over all total score, along with wealth and income data vs each response. 
# This data set is also sorted by wealth with all responses 'NA' removed.
# We also assign quartiles ranking each response according to this sort.

df4 = df2.sort_values(by = 'wealth')
df4 = df4.reset_index(drop = True)
df4 = df4.dropna(axis = 0, subset = 'wealth')
df4.insert(loc = len(df4.columns), column = "Quartile Number", value = pd.qcut(df4["wealth"],q = 4, labels = False ) + 1, allow_duplicates = 'False')

df4
Out[103]:
ResponseId age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_% Quartile Number
0 R_4MJAzsNYYA69Y8p 27.0 2 3 1 0.0 4 1 3 1 0 1 80.0 50.0 60.0 33.333333 0.0 100.0 10 55.555556 1
1 R_41bfnamM0zpH94i 30.0 2 1 2 0.0 4 0 2 1 1 1 80.0 0.0 40.0 33.333333 50.0 100.0 9 50.000000 1
2 e7ty2tbwa1hcmte7ty683aw64pla7689 30.5 2 2 1 0.0 1 0 2 1 1 0 20.0 0.0 40.0 33.333333 50.0 0.0 5 27.777778 1
3 R_4lnmGavSf6rw1eE 31.0 2 1 1 0.0 1 0 2 1 2 0 20.0 0.0 40.0 33.333333 100.0 0.0 6 33.333333 1
4 R_4DPGUOAP86yO30R 19.0 2 3 1 0.0 4 2 1 2 1 1 80.0 100.0 20.0 66.666667 50.0 100.0 11 61.111111 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
834 lmoqk1qcf44jqqudwkplmoqk18cao0fs 40.5 3 1 2 400000000.0 2 0 1 1 1 0 40.0 0.0 20.0 33.333333 50.0 0.0 5 27.777778 4
835 wn9mk6m1k8o10l1twn9mk6b4z7l7yvhc 50.5 3 1 3 500000000.0 2 0 3 3 2 0 40.0 0.0 60.0 100.000000 100.0 0.0 10 55.555556 4
836 oczyh23wpxpk4o0teoczy5zzzng3i6kk 30.5 2 2 5 500000000.0 1 0 3 3 2 0 20.0 0.0 60.0 100.000000 100.0 0.0 9 50.000000 4
837 35zux4sc18rplyz3dc85z35zux4sdfm4 21.5 2 2 4 500000000.0 2 0 1 0 1 0 40.0 0.0 20.0 0.000000 50.0 0.0 4 22.222222 4
838 zcj7ldokhyo6217f9sriwizcj7ldodwt 40.5 3 2 3 600000000.0 3 1 2 1 2 1 60.0 50.0 40.0 33.333333 100.0 100.0 10 55.555556 4

839 rows × 21 columns

In [104]:
# Trend line for Absolute Total Facet Score vs unique wealth reponses

sns.regplot (data = df4, x = df4.index, y = 'TotalScore_18')
Out[104]:
<Axes: ylabel='TotalScore_18'>
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In [105]:
# Trend line for Absolute Independent Facet Score vs wealth reponses

fig1, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4, x = df4.index, y = 'Certainty_5', fit_reg=True, ci=None, ax=ax1, label='Certainty_5')
sns.regplot (data = df4, x = df4.index, y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax1, label='RiskComprehension_5')
sns.regplot (data = df4, x = df4.index, y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax1, label='Numeracy_2')
sns.regplot (data = df4, x = df4.index, y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax1, label='GraphLiteracy_3')
sns.regplot (data = df4, x = df4.index, y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax1, label='Bayesianreasoning_1' )

ax1.set(ylabel='Scores', xlabel='wealth')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [106]:
# Count of number of responses under each quartile

df4.groupby('Quartile Number')[['Quartile Number']].count()
Out[106]:
Quartile Number
Quartile Number
1 221
2 252
3 195
4 171
In [ ]:
 
In [107]:
# Absolute Total Facet scores mean line plot from low to high Wealth

df4.groupby('Quartile Number')[['TotalScore_18']].mean().plot( kind = 'line', title = 'Absolute Total Facet scores mean line plot low to high wealth', xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[107]:
<matplotlib.legend.Legend at 0x293e6086410>
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In [108]:
# Absolute Facet scores mean line plot from low to high wealth

df4.groupby('Quartile Number')[['Certainty_5', "Uncertainty_2", 'RiskComprehension_5','Numeracy_2','GraphLiteracy_3','Bayesianreasoning_1']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot low to high wealth', xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[108]:
<matplotlib.legend.Legend at 0x293e66c8c90>
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In [109]:
# Normalised Facet scores mean line plot from low to high wealth


df4.groupby('Quartile Number')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().plot( kind = 'line', title = '% Facet scores mean line plot from low to high wealth',  xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[109]:
<matplotlib.legend.Legend at 0x293e749cb50>
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In [110]:
# Normalised mean scores for each facet stacked

df4.groupby('Quartile Number')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%']].mean().plot( kind = 'bar', title = '% mean scores for each facet stacked', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[110]:
<matplotlib.legend.Legend at 0x293ec890ad0>
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In [111]:
# Normalised mean scores for each facet for each wealth response Quartile

df4.groupby('Quartile Number')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().T.plot(kind = 'bar', title = '% mean scores for each facet for each wealth response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[111]:
<matplotlib.legend.Legend at 0x293e73a59d0>
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In [112]:
# Trend line for Absolute Total Facet Score vs Quartiles

sns.regplot (data = df4, x = 'Quartile Number', y = 'TotalScore_18')
Out[112]:
<Axes: xlabel='Quartile Number', ylabel='TotalScore_18'>
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In [113]:
# Trend line for Absolute Independent Facet Score vs Quartiles

fig2, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4, x = 'Quartile Number', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax2, label='Certainty_5')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Uncertainty_2', fit_reg=True, ci=None, ax=ax2, label='Uncertainty_2')
sns.regplot (data = df4, x = 'Quartile Number', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax2, label='RiskComprehension_5')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax2, label='Numeracy_2')
sns.regplot (data = df4, x = 'Quartile Number', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax2, label='GraphLiteracy_3')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax2, label='Bayesianreasoning_1' )

ax2.set(ylabel='Scores', xlabel='wealth')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [114]:
# Violine Plot for TotalScore_18 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'TotalScore_18')
Out[114]:
<Axes: xlabel='Quartile Number', ylabel='TotalScore_18'>
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In [115]:
# Violine Plot for Certainty_5 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Certainty_5')
Out[115]:
<Axes: xlabel='Quartile Number', ylabel='Certainty_5'>
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In [116]:
# Violine Plot for Certainty_5 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Uncertainty_2')
Out[116]:
<Axes: xlabel='Quartile Number', ylabel='Uncertainty_2'>
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In [117]:
# Violine Plot for RiskComprehension_5 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'RiskComprehension_5')
Out[117]:
<Axes: xlabel='Quartile Number', ylabel='RiskComprehension_5'>
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In [118]:
# Violine Plot for GraphLiteracy_3 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'GraphLiteracy_3')
Out[118]:
<Axes: xlabel='Quartile Number', ylabel='GraphLiteracy_3'>
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In [119]:
# Violine Plot for Numeracy_2 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Numeracy_2')
Out[119]:
<Axes: xlabel='Quartile Number', ylabel='Numeracy_2'>
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In [120]:
# Violine Plot for Bayesianreasoning_1 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Bayesianreasoning_1')
Out[120]:
<Axes: xlabel='Quartile Number', ylabel='Bayesianreasoning_1'>
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In [ ]:
 
In [ ]:
 

WEALTH vs SCORES w/ ISCED classification¶

In [121]:
# Descriptive stats for the data set, isced = 1
# NA values of wealth are removed

df6 = df5.dropna(axis = 0, subset = 'wealth')
# df6.drop(df6[df6['wealth'] == 3500000].index, inplace = True)
df6.loc[df6['isced']==1][['wealth']].describe()
Out[121]:
wealth
count 4.130000e+02
mean 1.020335e+07
std 3.794182e+07
min 0.000000e+00
25% 3.000000e+05
50% 1.200000e+06
75% 5.000000e+06
max 5.000000e+08
In [122]:
# Descriptive stats for the data set, isced = 2

df6.loc[df6['isced']==2][['wealth']].describe()
Out[122]:
wealth
count 3.640000e+02
mean 9.861171e+06
std 5.225548e+07
min 0.000000e+00
25% 2.000000e+05
50% 6.000000e+05
75% 3.000000e+06
max 6.000000e+08
In [123]:
# Descriptive stats for the data set, isced = 3

df6.loc[df6['isced']==3][['wealth']].describe()
Out[123]:
wealth
count 6.200000e+01
mean 3.719399e+06
std 1.336024e+07
min 0.000000e+00
25% 5.000000e+04
50% 2.000000e+05
75% 1.500000e+06
max 1.000000e+08
In [124]:
# Trend line for Absolute Tota Facet Score vs wealth reponses sorted by isced and ORDERED by wealth WITH scatter

fig, ax7 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax7, label='ISCED = 1')
sns.regplot (data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax7, label='ISCED = 2')
sns.regplot (data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax7, label='ISCED = 3')

ax7.set(ylabel='Total Scores_19', xlabel='Wealth')
ax7.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [125]:
# Trend line for Absolute Tota Facet Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax8 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 3')

ax8.set(ylabel='Total Scores_19', xlabel='Wealth')
ax8.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [126]:
# Trend line for Absolute Certainty Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax9 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 3')

ax9.set(ylabel='Certainty_5', xlabel='Wealth')
ax9.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [127]:
# Trend line for Absolute Uncertainty Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax10 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 3')

ax10.set(ylabel='RiskComprehension_5', xlabel='Wealth')
ax10.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [128]:
# Trend line for Absolute Number Comprehension Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Numeracy_2', xlabel='Wealth')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [129]:
# Trend line for Absolute Graph Comprehension Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='GraphLiteracy_3', xlabel='Wealth')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [130]:
# Trend line for Absolute Bayesian Reasoning Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax12 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 3')

ax12.set(ylabel='Bayesianreasoning_1', xlabel='Wealth')
ax12.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
 
In [ ]:
 

WEALTH Quartile Number vs SCORES w/ ISCED and AGE based classification¶

In [131]:
# Descriptive stats for the data set, isced = 1
# NA values of wealth are removed

df4.loc[df4['isced']==1].describe()
Out[131]:
age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_% Quartile Number
count 413.000000 413.000000 413.0 413.000000 4.130000e+02 413.000000 413.000000 413.000000 413.000000 413.000000 413.000000 413.000000 413.000000 413.000000 413.000000 413.000000 413.000000 413.000000 413.000000 413.000000
mean 35.285714 2.443099 1.0 2.314770 1.020335e+07 2.249395 0.455206 2.205811 1.336562 1.077482 0.196126 44.987893 22.760291 44.116223 44.552058 53.874092 19.612591 7.520581 41.781006 2.544794
std 9.828602 0.582747 0.0 1.091761 3.794182e+07 1.293479 0.616168 1.069781 0.963179 0.771961 0.397546 25.869582 30.808415 21.395627 32.105960 38.598049 39.754648 2.523229 14.017942 1.084334
min 18.000000 2.000000 1.0 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2.000000 11.111111 1.000000
25% 30.500000 2.000000 1.0 2.000000 3.000000e+05 1.000000 0.000000 1.000000 1.000000 0.000000 0.000000 20.000000 0.000000 20.000000 33.333333 0.000000 0.000000 6.000000 33.333333 2.000000
50% 30.500000 2.000000 1.0 2.000000 1.200000e+06 2.000000 0.000000 2.000000 1.000000 1.000000 0.000000 40.000000 0.000000 40.000000 33.333333 50.000000 0.000000 7.000000 38.888889 3.000000
75% 40.500000 3.000000 1.0 3.000000 5.000000e+06 3.000000 1.000000 3.000000 2.000000 2.000000 0.000000 60.000000 50.000000 60.000000 66.666667 100.000000 0.000000 9.000000 50.000000 3.000000
max 70.500000 4.000000 1.0 5.000000 5.000000e+08 5.000000 2.000000 5.000000 3.000000 2.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 15.000000 83.333333 4.000000
In [132]:
df4.loc[df4['isced']==2].describe()
Out[132]:
age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_% Quartile Number
count 364.000000 364.000000 364.0 364.000000 3.640000e+02 364.000000 364.000000 364.000000 364.000000 364.000000 364.000000 364.000000 364.000000 364.000000 364.000000 364.000000 364.000000 364.000000 364.000000 364.000000
mean 30.877747 2.271978 2.0 1.851648 9.861171e+06 2.637363 0.447802 2.214286 1.376374 1.090659 0.255495 52.747253 22.390110 44.285714 45.879121 54.532967 25.549451 8.021978 44.566545 2.274725
std 10.056769 0.514458 0.0 1.017734 5.225548e+07 1.323794 0.612110 1.019487 0.995080 0.823231 0.436739 26.475871 30.605488 20.389748 33.169331 41.161531 43.673914 2.646180 14.701002 1.042522
min 18.000000 2.000000 2.0 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 5.555556 1.000000
25% 22.000000 2.000000 2.0 1.000000 2.000000e+05 1.000000 0.000000 2.000000 1.000000 0.000000 0.000000 20.000000 0.000000 40.000000 33.333333 0.000000 0.000000 6.000000 33.333333 1.000000
50% 30.500000 2.000000 2.0 2.000000 6.000000e+05 3.000000 0.000000 2.000000 1.000000 1.000000 0.000000 60.000000 0.000000 40.000000 33.333333 50.000000 0.000000 8.000000 44.444444 2.000000
75% 34.000000 2.000000 2.0 2.000000 3.000000e+06 4.000000 1.000000 3.000000 2.000000 2.000000 1.000000 80.000000 50.000000 60.000000 66.666667 100.000000 100.000000 10.000000 55.555556 3.000000
max 70.000000 4.000000 2.0 5.000000 6.000000e+08 5.000000 2.000000 5.000000 3.000000 2.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 15.000000 83.333333 4.000000
In [133]:
df4.loc[df4['isced']==3].describe()
Out[133]:
age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_% Quartile Number
count 62.000000 62.000000 62.0 62.000000 6.200000e+01 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000
mean 26.951613 2.225806 3.0 1.629032 3.719399e+06 3.145161 0.677419 2.080645 1.322581 0.967742 0.225806 62.903226 33.870968 41.612903 44.086022 48.387097 22.580645 8.419355 46.774194 1.854839
std 12.274093 0.584481 0.0 1.119629 1.336024e+07 1.157255 0.672022 0.892563 0.971296 0.829136 0.421526 23.145094 33.601075 17.851257 32.376538 41.456813 42.152552 2.583354 14.351966 1.068886
min 16.000000 0.000000 3.0 1.000000 0.000000e+00 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 20.000000 0.000000 0.000000 0.000000 0.000000 0.000000 3.000000 16.666667 1.000000
25% 19.000000 2.000000 3.0 1.000000 5.000000e+04 2.000000 0.000000 2.000000 1.000000 0.000000 0.000000 40.000000 0.000000 40.000000 33.333333 0.000000 0.000000 7.000000 38.888889 1.000000
50% 21.250000 2.000000 3.0 1.000000 2.000000e+05 4.000000 1.000000 2.000000 1.000000 1.000000 0.000000 80.000000 50.000000 40.000000 33.333333 50.000000 0.000000 9.000000 50.000000 1.000000
75% 30.500000 2.000000 3.0 2.000000 1.500000e+06 4.000000 1.000000 3.000000 2.000000 2.000000 0.000000 80.000000 50.000000 60.000000 66.666667 100.000000 0.000000 10.000000 55.555556 3.000000
max 70.500000 4.000000 3.0 5.000000 1.000000e+08 5.000000 2.000000 4.000000 3.000000 2.000000 1.000000 100.000000 100.000000 80.000000 100.000000 100.000000 100.000000 12.000000 66.666667 4.000000
In [134]:
# Trend line for Absolute Tota Facet Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITH scatter

fig, ax7 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[df4['isced']==1]['TotalScore_18'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 1')
sns.regplot (data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[df4['isced']==2]['TotalScore_18'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 2')
sns.regplot (data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[df4['isced']==3]['TotalScore_18'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 3')

ax7.set(ylabel='Total Scores_19', xlabel='WEALTH Quartile Number')
ax7.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [135]:
# Trend line for Absolute Tota Facet Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITHOUT scatter

fig, ax8 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 3')

ax8.set(ylabel='Total Scores_19', xlabel='WEALTH Quartile Number')
ax8.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [136]:
# Trend line for Absolute Certainty Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITHOUT scatter

fig, ax9 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 3')

ax9.set(ylabel='Certainty_5', xlabel='WEALTH Quartile Number')
ax9.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [137]:
# Trend line for Absolute Risk Comprehension Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITHOUT scatter

fig, ax10 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 3')

ax10.set(ylabel='RiskComprehension_5', xlabel='WEALTH Quartile Number')
ax10.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [138]:
# Trend line for Absolute Number Comprehension Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Numeracy_2', xlabel='WEALTH Quartile Number')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [139]:
# Trend line for Absolute Graph Comprehension Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='GraphLiteracy_3', xlabel='WEALTH Quartile Number')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [140]:
# Trend line for Absolute Bayesian Reasoning Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITHOUT scatter

fig, ax12 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 3')

ax12.set(ylabel='Bayesianreasoning_1', xlabel='WEALTH Quartile Number')
ax12.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
 
In [ ]:
 
In [ ]:
 
In [141]:
df4
Out[141]:
ResponseId age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_% Quartile Number
0 R_4MJAzsNYYA69Y8p 27.0 2 3 1 0.0 4 1 3 1 0 1 80.0 50.0 60.0 33.333333 0.0 100.0 10 55.555556 1
1 R_41bfnamM0zpH94i 30.0 2 1 2 0.0 4 0 2 1 1 1 80.0 0.0 40.0 33.333333 50.0 100.0 9 50.000000 1
2 e7ty2tbwa1hcmte7ty683aw64pla7689 30.5 2 2 1 0.0 1 0 2 1 1 0 20.0 0.0 40.0 33.333333 50.0 0.0 5 27.777778 1
3 R_4lnmGavSf6rw1eE 31.0 2 1 1 0.0 1 0 2 1 2 0 20.0 0.0 40.0 33.333333 100.0 0.0 6 33.333333 1
4 R_4DPGUOAP86yO30R 19.0 2 3 1 0.0 4 2 1 2 1 1 80.0 100.0 20.0 66.666667 50.0 100.0 11 61.111111 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
834 lmoqk1qcf44jqqudwkplmoqk18cao0fs 40.5 3 1 2 400000000.0 2 0 1 1 1 0 40.0 0.0 20.0 33.333333 50.0 0.0 5 27.777778 4
835 wn9mk6m1k8o10l1twn9mk6b4z7l7yvhc 50.5 3 1 3 500000000.0 2 0 3 3 2 0 40.0 0.0 60.0 100.000000 100.0 0.0 10 55.555556 4
836 oczyh23wpxpk4o0teoczy5zzzng3i6kk 30.5 2 2 5 500000000.0 1 0 3 3 2 0 20.0 0.0 60.0 100.000000 100.0 0.0 9 50.000000 4
837 35zux4sc18rplyz3dc85z35zux4sdfm4 21.5 2 2 4 500000000.0 2 0 1 0 1 0 40.0 0.0 20.0 0.000000 50.0 0.0 4 22.222222 4
838 zcj7ldokhyo6217f9sriwizcj7ldodwt 40.5 3 2 3 600000000.0 3 1 2 1 2 1 60.0 50.0 40.0 33.333333 100.0 100.0 10 55.555556 4

839 rows × 21 columns

In [142]:
# Since we already have a classification for Age groups in the form of age_rec, we will use that.
# We will also use median of age to see if it yields any relevant results, as instructed.

# AXES to be used = Age or age groups, ISCED, Income

# Age groups = [2,3,4]
# Age group 2 = 18 to 35 y/o
# Age group 3 = 36 to 59 y/o
# Age group 4 = 60 to 75 y/o (75 y/o, i.e, within the scope of the data we have, it can mean 60 and above also)
In [143]:
df4.loc[(df4['age_rec']==2)].describe()
Out[143]:
age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_% Quartile Number
count 573.000000 573.0 573.000000 573.000000 5.730000e+02 573.000000 573.000000 573.000000 573.000000 573.000000 573.000000 573.000000 573.000000 573.000000 573.000000 573.000000 573.000000 573.000000 573.000000 573.000000
mean 26.943281 2.0 1.647469 1.959860 7.616613e+06 2.554974 0.476440 2.158813 1.371728 1.085515 0.235602 51.099476 23.821990 43.176265 45.724258 54.275742 23.560209 7.883072 43.794842 2.249564
std 4.633303 0.0 0.626887 1.047872 3.540355e+07 1.350679 0.638141 1.016084 0.973263 0.805151 0.424745 27.013572 31.907039 20.321679 32.442106 40.257572 42.474533 2.596786 14.426588 1.035483
min 18.000000 2.0 1.000000 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 5.555556 1.000000
25% 22.000000 2.0 1.000000 1.000000 2.000000e+05 1.000000 0.000000 1.000000 1.000000 0.000000 0.000000 20.000000 0.000000 20.000000 33.333333 0.000000 0.000000 6.000000 33.333333 1.000000
50% 30.000000 2.0 2.000000 2.000000 6.500000e+05 3.000000 0.000000 2.000000 1.000000 1.000000 0.000000 60.000000 0.000000 40.000000 33.333333 50.000000 0.000000 8.000000 44.444444 2.000000
75% 30.500000 2.0 2.000000 2.000000 3.000000e+06 4.000000 1.000000 3.000000 2.000000 2.000000 0.000000 80.000000 50.000000 60.000000 66.666667 100.000000 0.000000 10.000000 55.555556 3.000000
max 35.000000 2.0 3.000000 5.000000 5.000000e+08 5.000000 2.000000 5.000000 3.000000 2.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 15.000000 83.333333 4.000000
In [144]:
df4.loc[(df4['age_rec']==3)].describe()
Out[144]:
age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_% Quartile Number
count 232.000000 232.0 232.000000 232.000000 2.320000e+02 232.000000 232.000000 232.000000 232.000000 232.000000 232.000000 232.000000 232.000000 232.000000 232.000000 232.00000 232.000000 232.000000 232.000000 232.000000
mean 42.892241 3.0 1.426724 2.306034 1.445481e+07 2.314655 0.448276 2.271552 1.284483 1.047414 0.198276 46.293103 22.413793 45.431034 42.816092 52.37069 19.827586 7.564655 42.025862 2.581897
std 4.753812 0.0 0.591258 1.138221 6.124404e+07 1.282635 0.578771 1.048380 0.987419 0.790853 0.399563 25.652698 28.938528 20.967600 32.913966 39.54263 39.956313 2.591219 14.395662 1.132832
min 36.000000 3.0 1.000000 1.000000 3.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 2.000000 11.111111 1.000000
25% 40.500000 3.0 1.000000 1.750000 3.000000e+05 1.000000 0.000000 2.000000 0.000000 0.000000 0.000000 20.000000 0.000000 40.000000 0.000000 0.00000 0.000000 6.000000 33.333333 2.000000
50% 40.500000 3.0 1.000000 2.000000 1.750000e+06 2.000000 0.000000 2.000000 1.000000 1.000000 0.000000 40.000000 0.000000 40.000000 33.333333 50.00000 0.000000 8.000000 44.444444 3.000000
75% 46.500000 3.0 2.000000 3.000000 7.500000e+06 4.000000 1.000000 3.000000 2.000000 2.000000 0.000000 80.000000 50.000000 60.000000 66.666667 100.00000 0.000000 9.000000 50.000000 4.000000
max 54.000000 3.0 3.000000 5.000000 6.000000e+08 5.000000 2.000000 5.000000 3.000000 2.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.00000 100.000000 14.000000 77.777778 4.000000
In [145]:
df4.loc[(df4['age_rec']==4)].describe()
Out[145]:
age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_% Quartile Number
count 33.000000 33.0 33.000000 33.000000 3.300000e+01 33.000000 33.000000 33.000000 33.000000 33.000000 33.000000 33.000000 33.000000 33.000000 33.000000 33.000000 33.000000 33.000000 33.000000 33.000000
mean 62.969697 4.0 1.484848 2.181818 9.582270e+06 2.393939 0.454545 2.393939 1.545455 1.090909 0.212121 47.878788 22.727273 47.878788 51.515152 54.545455 21.212121 8.090909 44.949495 3.181818
std 4.390934 0.0 0.618527 1.236288 1.406214e+07 0.966288 0.616994 1.248484 0.938446 0.765001 0.415149 19.325756 30.849709 24.969679 31.281550 38.250074 41.514875 2.602446 14.458036 0.950478
min 57.000000 4.0 1.000000 1.000000 1.000000e+05 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 20.000000 0.000000 0.000000 0.000000 0.000000 0.000000 3.000000 16.666667 1.000000
25% 60.500000 4.0 1.000000 1.000000 1.250000e+06 2.000000 0.000000 1.000000 1.000000 1.000000 0.000000 40.000000 0.000000 20.000000 33.333333 50.000000 0.000000 6.000000 33.333333 3.000000
50% 60.500000 4.0 1.000000 2.000000 5.000000e+06 2.000000 0.000000 2.000000 2.000000 1.000000 0.000000 40.000000 0.000000 40.000000 66.666667 50.000000 0.000000 8.000000 44.444444 3.000000
75% 65.000000 4.0 2.000000 2.000000 1.000000e+07 3.000000 1.000000 3.000000 2.000000 2.000000 0.000000 60.000000 50.000000 60.000000 66.666667 100.000000 0.000000 10.000000 55.555556 4.000000
max 70.500000 4.0 3.000000 5.000000 5.250000e+07 4.000000 2.000000 5.000000 3.000000 2.000000 1.000000 80.000000 100.000000 100.000000 100.000000 100.000000 100.000000 13.000000 72.222222 4.000000
In [ ]:
 
In [146]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_14', xlabel='WEALTH Quartile Number for age 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_14', xlabel='WEALTH Quartile Number for age 36 to 55')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_14', xlabel='WEALTH Quartile Number for age 56 and above')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [147]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ df4['age_rec']==2  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==2  ].groupby(['Quartile Number']).mean(numeric_only=True)['TotalScore_18'], yerr = df4.loc[ (df4['age_rec']==2) ].groupby(['Quartile Number'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Total Scores_14', xlabel='WEALTH Quartile Number for age 18 to 35',  yticks = np.arange(5, 16 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_14', xlabel='WEALTH Quartile Number for age 36 to 55',  yticks = np.arange(5, 16 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df4.loc[ df4['age_rec']==3  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==3  ].groupby(['Quartile Number']).mean(numeric_only=True)['TotalScore_18'], yerr = df4.loc[ (df4['age_rec']==3) ].groupby(['Quartile Number'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_14', xlabel='WEALTH Quartile Number for age 56 and above',  yticks = np.arange(5, 16 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df4.loc[ df4['age_rec']== 4 ]['Quartile Number'].unique(), y = df4.loc[ df4['age_rec']==4  ].groupby(['Quartile Number']).mean(numeric_only=True)['TotalScore_18'], yerr = df4.loc[ (df4['age_rec']==4) ].groupby(['Quartile Number'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [148]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Certainty_5', xlabel='WEALTH Quartile Number for age 18 to 35',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df4.loc[ df4['age_rec']==2  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==2  ].groupby(['Quartile Number'])['Certainty_5'].mean(), yerr = df4.loc[ (df4['age_rec']==2) ].groupby(['Quartile Number'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Certainty_5', xlabel='WEALTH Quartile Number for age 36 to 55',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df4.loc[ df4['age_rec']==3  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==3  ].groupby(['Quartile Number'])['Certainty_5'].mean(), yerr = df4.loc[ (df4['age_rec']==3) ].groupby(['Quartile Number'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Certainty_5', xlabel='WEALTH Quartile Number for age 56 and above',  yticks = np.arange(0, 6 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df4.loc[ df4['age_rec']== 4 ]['Quartile Number'].unique(), y = df4.loc[ df4['age_rec']==4  ].groupby(['Quartile Number'])['Certainty_5'].mean(), yerr = df4.loc[ (df4['age_rec']==4) ].groupby(['Quartile Number'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [149]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='RiskComprehension_5', xlabel='WEALTH Quartile Number for age 18 to 35',  yticks = np.arange(0, 3 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df4.loc[ df4['age_rec']==2  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==2  ].groupby(['Quartile Number'])['RiskComprehension_5'].mean(), yerr = df4.loc[ (df4['age_rec']==2) ].groupby(['Quartile Number'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='RiskComprehension_5', xlabel='WEALTH Quartile Number for age 36 to 55',  yticks = np.arange(0, 3 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df4.loc[ df4['age_rec']==3  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==3  ].groupby(['Quartile Number'])['RiskComprehension_5'].mean(), yerr = df4.loc[ (df4['age_rec']==3) ].groupby(['Quartile Number'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='RiskComprehension_5', xlabel='WEALTH Quartile Number for age 56 and above',  yticks = np.arange(0, 3 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df4.loc[ df4['age_rec']== 4 ]['Quartile Number'].unique(), y = df4.loc[ df4['age_rec']==4  ].groupby(['Quartile Number'])['RiskComprehension_5'].mean(), yerr = df4.loc[ (df4['age_rec']==4) ].groupby(['Quartile Number'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [150]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Numeracy_2', xlabel='WEALTH Quartile Number for age 18 to 35',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df4.loc[ df4['age_rec']==2  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==2  ].groupby(['Quartile Number'])['Numeracy_2'].mean(), yerr = df4.loc[ (df4['age_rec']==2) ].groupby(['Quartile Number'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Numeracy_2', xlabel='WEALTH Quartile Number for age 36 to 55',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df4.loc[ df4['age_rec']==3  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==3  ].groupby(['Quartile Number'])['Numeracy_2'].mean(), yerr = df4.loc[ (df4['age_rec']==3) ].groupby(['Quartile Number'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Numeracy_2', xlabel='WEALTH Quartile Number for age 56 and above',  yticks = np.arange(0, 6 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df4.loc[ df4['age_rec']== 4 ]['Quartile Number'].unique(), y = df4.loc[ df4['age_rec']==4  ].groupby(['Quartile Number'])['Numeracy_2'].mean(), yerr = df4.loc[ (df4['age_rec']==4) ].groupby(['Quartile Number'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [151]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='GraphLiteracy_3', xlabel='WEALTH Quartile Number for age 18 to 35',  yticks = np.arange(0, 4 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df4.loc[ df4['age_rec']==2  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==2  ].groupby(['Quartile Number'])['GraphLiteracy_3'].mean(), yerr = df4.loc[ (df4['age_rec']==2) ].groupby(['Quartile Number'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='GraphLiteracy_3', xlabel='WEALTH Quartile Number for age 36 to 55',  yticks = np.arange(0, 4 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df4.loc[ df4['age_rec']==3  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==3  ].groupby(['Quartile Number'])['GraphLiteracy_3'].mean(), yerr = df4.loc[ (df4['age_rec']==3) ].groupby(['Quartile Number'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='GraphLiteracy_3', xlabel='WEALTH Quartile Number for age 56 and above',  yticks = np.arange(0, 4 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df4.loc[ df4['age_rec']== 4 ]['Quartile Number'].unique(), y = df4.loc[ df4['age_rec']==4  ].groupby(['Quartile Number'])['GraphLiteracy_3'].mean(), yerr = df4.loc[ (df4['age_rec']==4) ].groupby(['Quartile Number'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [152]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Bayesianreasoning_1', xlabel='WEALTH Quartile Number for age 18 to 35',  yticks = np.arange(0, 5 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df4.loc[ df4['age_rec']==2  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==2  ].groupby(['Quartile Number'])['Bayesianreasoning_1'].mean(), yerr = df4.loc[ (df4['age_rec']==2) ].groupby(['Quartile Number'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Bayesianreasoning_1', xlabel='WEALTH Quartile Number for age 36 to ',  yticks = np.arange(0, 5 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df4.loc[ df4['age_rec']==3  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==3  ].groupby(['Quartile Number'])['Bayesianreasoning_1'].mean(), yerr = df4.loc[ (df4['age_rec']==3) ].groupby(['Quartile Number'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Bayesianreasoning_1', xlabel='WEALTH Quartile Number for age 56 and above',  yticks = np.arange(0, 5 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df4.loc[ df4['age_rec']== 4 ]['Quartile Number'].unique(), y = df4.loc[ df4['age_rec']==4  ].groupby(['Quartile Number'])['Bayesianreasoning_1'].mean(), yerr = df4.loc[ (df4['age_rec']==4) ].groupby(['Quartile Number'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [153]:
# Age mean method
# Age_mean
In [154]:
age_m = df2["age"].median()
age_m
Out[154]:
30.5
In [ ]:
 
In [155]:
df4["Quartile Number"].value_counts()
Out[155]:
Quartile Number
2    252
1    221
3    195
4    171
Name: count, dtype: int64
In [ ]:
 
In [ ]:
 
In [156]:
df4.loc[(df4['age'] < age_m)].describe()
Out[156]:
age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_% Quartile Number
count 298.000000 298.000000 298.000000 298.000000 2.980000e+02 298.000000 298.000000 298.000000 298.000000 298.000000 298.000000 298.000000 298.000000 298.000000 298.000000 298.000000 298.000000 298.000000 298.000000 298.000000
mean 23.206376 1.993289 1.869128 1.788591 7.382464e+06 3.036913 0.553691 2.171141 1.459732 1.151007 0.271812 60.738255 27.684564 43.422819 48.657718 57.550336 27.181208 8.644295 48.023863 2.073826
std 3.435092 0.115857 0.635366 1.050383 3.704148e+07 1.248021 0.665759 0.939718 0.984761 0.804459 0.445642 24.960412 33.287967 18.794362 32.825354 40.222937 44.564190 2.504068 13.911487 1.032104
min 16.000000 0.000000 1.000000 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2.000000 11.111111 1.000000
25% 21.000000 2.000000 1.000000 1.000000 1.000000e+05 2.000000 0.000000 2.000000 1.000000 0.000000 0.000000 40.000000 0.000000 40.000000 33.333333 0.000000 0.000000 7.000000 38.888889 1.000000
50% 22.000000 2.000000 2.000000 1.000000 5.000000e+05 4.000000 0.000000 2.000000 1.000000 1.000000 0.000000 80.000000 0.000000 40.000000 33.333333 50.000000 0.000000 9.000000 50.000000 2.000000
75% 26.000000 2.000000 2.000000 2.000000 2.000000e+06 4.000000 1.000000 3.000000 2.000000 2.000000 1.000000 80.000000 50.000000 60.000000 66.666667 100.000000 100.000000 10.000000 55.555556 3.000000
max 30.000000 2.000000 3.000000 5.000000 5.000000e+08 4.000000 2.000000 5.000000 3.000000 2.000000 1.000000 80.000000 100.000000 100.000000 100.000000 100.000000 100.000000 15.000000 83.333333 4.000000
In [157]:
df4.loc[(df4['age'] >= age_m)].describe()
Out[157]:
age age_rec isced income wealth Certainty_5 Uncertainty_2 RiskComprehension_5 GraphLiteracy_3 Numeracy_2 Bayesianreasoning_1 Certainty_% Uncertainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_18 TotalScore_% Quartile Number
count 541.000000 541.000000 541.000000 541.000000 5.410000e+02 541.000000 541.000000 541.000000 541.000000 541.000000 541.000000 541.000000 541.000000 541.000000 541.000000 541.000000 541.000000 541.000000 541.000000 541.000000
mean 38.018484 2.550832 1.423290 2.214418 1.078387e+07 2.179298 0.421442 2.216266 1.293900 1.033272 0.197782 43.585952 21.072089 44.325323 43.130006 51.663586 19.778189 7.341959 40.788663 2.543438
std 9.235180 0.608356 0.561193 1.084510 4.690035e+07 1.263159 0.589902 1.084996 0.968265 0.792793 0.398695 25.263180 29.495078 21.699921 32.275495 39.639645 39.869541 2.530458 14.058100 1.073441
min 30.500000 2.000000 1.000000 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 5.555556 1.000000
25% 30.500000 2.000000 1.000000 1.000000 3.000000e+05 1.000000 0.000000 1.000000 1.000000 0.000000 0.000000 20.000000 0.000000 20.000000 33.333333 0.000000 0.000000 6.000000 33.333333 2.000000
50% 35.000000 2.000000 1.000000 2.000000 1.200000e+06 2.000000 0.000000 2.000000 1.000000 1.000000 0.000000 40.000000 0.000000 40.000000 33.333333 50.000000 0.000000 7.000000 38.888889 3.000000
75% 40.500000 3.000000 2.000000 3.000000 5.000000e+06 3.000000 1.000000 3.000000 2.000000 2.000000 0.000000 60.000000 50.000000 60.000000 66.666667 100.000000 0.000000 9.000000 50.000000 3.000000
max 70.500000 4.000000 3.000000 5.000000 6.000000e+08 5.000000 2.000000 5.000000 3.000000 2.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 14.000000 77.777778 4.000000
In [ ]:
 
In [158]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_19', xlabel='WEALTH Quartile Number for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[(df4['age'] >= age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_19', xlabel='WEALTH Quartile Number for >=median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [159]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ (df4['age'] < age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] < age_m)  ].groupby(['Quartile Number'])['TotalScore_18'].mean(), yerr = df4.loc[ (df4['age'] < age_m) ].groupby(['Quartile Number'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Total Scores_19', xlabel='WEALTH Quartile Number for < median age',  yticks = np.arange(5, 16 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df4.loc[ (df4['age'] >= age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] >=age_m)  ].groupby(['Quartile Number'])['TotalScore_18'].mean(), yerr = df4.loc[ (df4['age'] >=age_m) ].groupby(['Quartile Number'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Total Scores_19', xlabel='WEALTH Quartile Number for >=median age',  yticks = np.arange(5, 16 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [160]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ (df4['age'] < age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] < age_m)  ].groupby(['Quartile Number'])['Certainty_5'].mean(), yerr = df4.loc[ (df4['age'] < age_m) ].groupby(['Quartile Number'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Certainty_5', xlabel='WEALTH Quartile Number for < median age',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df4.loc[ (df4['age'] >= age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] >=age_m)  ].groupby(['Quartile Number'])['Certainty_5'].mean(), yerr = df4.loc[ (df4['age'] >=age_m) ].groupby(['Quartile Number'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Certainty_5', xlabel='WEALTH Quartile Number for >=median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [161]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ (df4['age'] < age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] < age_m)  ].groupby(['Quartile Number'])['RiskComprehension_5'].mean(), yerr = df4.loc[ (df4['age'] < age_m) ].groupby(['Quartile Number'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='RiskComprehension_5', xlabel='WEALTH Quartile Number for < median age',  yticks = np.arange(0, 3 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df4.loc[ (df4['age'] >= age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] >=age_m)  ].groupby(['Quartile Number'])['RiskComprehension_5'].mean(), yerr = df4.loc[ (df4['age'] >=age_m) ].groupby(['Quartile Number'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='RiskComprehension_5', xlabel='WEALTH Quartile Number for >=median age',  yticks = np.arange(0, 3 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [162]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ (df4['age'] < age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] < age_m)  ].groupby(['Quartile Number'])['Numeracy_2'].mean(), yerr = df4.loc[ (df4['age'] < age_m) ].groupby(['Quartile Number'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Numeracy_2', xlabel='WEALTH Quartile Number for < median age',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df4.loc[ (df4['age'] >= age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] >=age_m)  ].groupby(['Quartile Number'])['Numeracy_2'].mean(), yerr = df4.loc[ (df4['age'] >=age_m) ].groupby(['Quartile Number'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Numeracy_2', xlabel='WEALTH Quartile Number for >=median age',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [163]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ (df4['age'] < age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] < age_m)  ].groupby(['Quartile Number'])['GraphLiteracy_3'].mean(), yerr = df4.loc[ (df4['age'] < age_m) ].groupby(['Quartile Number'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='GraphLiteracy_3', xlabel='WEALTH Quartile Number for < median age',  yticks = np.arange(0, 4 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df4.loc[ (df4['age'] >= age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] >=age_m)  ].groupby(['Quartile Number'])['GraphLiteracy_3'].mean(), yerr = df4.loc[ (df4['age'] >=age_m) ].groupby(['Quartile Number'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='GraphLiteracy_3', xlabel='WEALTH Quartile Number for >=median age',  yticks = np.arange(0, 4 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [164]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ (df4['age'] < age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] < age_m)  ].groupby(['Quartile Number'])['Bayesianreasoning_1'].mean(), yerr = df4.loc[ (df4['age'] < age_m) ].groupby(['Quartile Number'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Bayesianreasoning_1', xlabel='WEALTH Quartile Number for < median age',  yticks = np.arange(0, 5 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df4.loc[ (df4['age'] >= age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] >=age_m)  ].groupby(['Quartile Number'])['Bayesianreasoning_1'].mean(), yerr = df4.loc[ (df4['age'] >=age_m) ].groupby(['Quartile Number'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Bayesianreasoning_1', xlabel='WEALTH Quartile Number for >=median age',  yticks = np.arange(0, 5 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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